# A Framework for Scalable Ambient Air Pollution Concentration Estimation

Liam J. Berrisford1,2,3,∗
<EMAIL_ADDRESS>Lucy S. Neal4
<EMAIL_ADDRESS>Helen J. Buttery4
<EMAIL_ADDRESS>Benjamin R. Evans4
<EMAIL_ADDRESS>Ronaldo Menezes1,5
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
(1 BioComplex Laboratory, Department of Computer Science, University of
Exeter, England
2 Department of Mathematics, University of Exeter, England
3 UKRI Centre for Doctoral Training in Environmental Intelligence, University
of Exeter, England
4 Met Office, FitzRoy Road, Exeter, EX1 3PB, U.K.
5 Department of Computer Science, Federal University of Ceará, Fortaleza,
Brazil
∗ Corresponding Author
)

###### Abstract

Ambient air pollution remains a critical issue in the United Kingdom, where
data on air pollution concentrations form the foundation for interventions
aimed at improving air quality. However, the current air pollution monitoring
station network in the UK is characterized by spatial sparsity, heterogeneous
placement, and frequent temporal data gaps, often due to issues such as power
outages. We introduce a scalable data-driven supervised machine learning model
framework designed to address temporal and spatial data gaps by filling
missing measurements. This approach provides a comprehensive dataset for
England throughout 2018 at a 1km2 hourly resolution. Leveraging machine
learning techniques and real-world data from the sparsely distributed
monitoring stations, we generate 355,827 synthetic monitoring stations across
the study area, yielding data valued at approximately £70 billion. Validation
was conducted to assess the model’s performance in forecasting, estimating
missing locations, and capturing peak concentrations. The resulting dataset is
of particular interest to a diverse range of stakeholders engaged in
downstream assessments supported by outdoor air pollution concentration data
for NO2, O3, PM10, PM2.5, and SO2. This resource empowers stakeholders to
conduct studies at a higher resolution than was previously possible.

Impact Statement: The current high-quality air pollution monitoring station
network in the UK is spatially sparse with heterogeneous placement and
commonly suffers from missing data temporally from issues such as power
outages. We present a scalable data-driven supervised machine learning model
framework to fill missing measurements temporally and spatially, providing a
complete dataset for England during 2018 at a 1kmx1km hourly resolution. The
approach leverages machine learning and data from the sparse real-world
monitoring stations to create 355,827 synthetic monitoring stations across the
study area to acquire data worth approximately £70 billion. Validation was
conducted regarding the model’s performance in forecasting, estimating missing
locations and capturing peak concentrations. The dataset provided is of
interest to a range of stakeholders conducting downstream assessments
underpinned by outdoor air pollution concentration data for NO2, O3, PM10,
PM25, and SO2, empowering stakeholders to perform studies at a higher
resolution than previously possible.

##  1 Introduction

Air pollution presents a significant health risk to individuals, with 28-36
thousand deaths per year in the UK associated with air pollution exposure [1].
Estimating ambient air pollution concentration levels is an essential step in
tackling the health burden of air pollution due to the high cost of individual
monitoring stations. The potential cost for a single multi-pollutant
monitoring station could be as high as £198,000 [2]. Even for a country such
as the United Kingdom that has highlighted tackling ambient air pollution as a
key priority [3], there are only 171 monitoring stations across the UK for all
pollutants currently being monitored111https://uk-
air.defra.gov.uk/networks/network-info?view=aurn. Therefore, areas without a
dedicated monitoring station need to have their ambient air pollution
concentrations estimated through models, which go on to inform policy for
interventions into air pollution, making the models used for this process of
pivotal importance.

Existing national-level datasets produce estimations at the annual temporal
scale and 1 km2 spatial resolution [4]. However, it is imperative to note that
health advisories, as articulated by organizations like the World Health
Organisation, delineate constraints not solely on the annual mean of air
pollution concentrations within a specified region but also on the daily mean.
To illustrate, for NO2, the stipulated limits include a 10
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ annual mean
and a 25 $\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$
24-hour limit [5], with the absence of an explicitly defined hourly limit [6].
The regulatory landscape in the United Kingdom, as governed by the Air Quality
Standard Regulations 2010 [7], delineates both limit values—legally binding
parameters not to be surpassed—and target values, akin to limit values but
lacking legal bindings. Notably, this legislation addresses hourly level means
for pollutants like NO2, with a meticulous limit of 200
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, not to be
exceeded more than 18 times in a year.

The prevailing methodology, limited to generating mean annual estimates at the
national scale, introduces a challenge. This stems from the fact that only
specific locales, equipped with monitoring stations, possess hourly data on
air pollution. Consequently, areas devoid of such monitoring infrastructure
are excluded from any analysis of air pollution levels at a more granular
temporal resolution. This discrepancy in data availability raises concerns
regarding health inequalities, underscoring the imperative need for a more
equitable and comprehensive approach.

The utilization of annual pollution levels provides a broad overview of the
pollution within a designated study area. However, a notable challenge arises
concerning information loss when transitioning from an hourly to a daily or
annual temporal scale. This issue has manifested in the United Kingdom, where
instances of divergent narratives emerge between the annual and daily means of
specific locations. Take, for instance, Leominster222https://uk-
air.defra.gov.uk/networks/site-info?site_id=LEOM on 03/12/2014 at 08:00, which
recorded a peak pollution value for NO2 of 80.2
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$. The 24-hour
mean in the vicinity of this peak, spanning 12 hours on either side (from
02/12/2014 2000 to 03/12/2014 2000), stands at 31.5
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, as
illustrated in Figure 1(a). This exceeds the WHO’s daily mean guideline of 25
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$. The
complexity deepens when examining Leominster’s annual mean for 2014,
registering a value of 9.5
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, deemed safe
by WHO guidelines and depicted in Figure 1(b). Similar disparities are
observable in other monitoring stations. For instance, London North Kensington
exhibits unsafe levels at both the annual and daily scales, with a peak value
of 209 $\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, a
daily mean of 122
$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, and an annual
mean of 33 $\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ for
the pollutant NO2. Table LABEL:S-tab:No2PeakValuesMeans details the peak
values for the five most polluted stations for NO2 within the study,
encompassing the Peak Value, Daily Mean surrounding the peak, and the annual
mean for the year of the peak occurrence.

While there is evident importance in hourly air pollution concentration data
for compliance and legislation purposes, the data serves a spectrum of other
critical purposes. Researchers, policymakers, and public health officials
routinely conduct human health assessments [8], enabling informed decisions
concerning interventions to protect vulnerable populations [9]. Further,
epidemiological studies assessments are routinely conducted [10] and are of
crucial importance when significant changes in air pollution are being
observed, such as during the COVID-19 pandemic [11]. Beyond human health,
ecosystem health can be significantly impacted by air pollution, leading to
damage to plants, manifested as leaf injury and stunted growth [12]. This has
critical implications for concerns related to crop yields and food security
[13].

As such, this work introduces a data-driven machine learning model designed to
estimate the hourly concentration of air pollutants at the same spatial
resolution (1 km2) as the existing annual dataset available for the UK 333UK-
AIR Annual Modelled Background Air Pollution Data.. We argue that this work
leads to a dataset of substantial value to various stakeholders.

(a) Leominster day air pollution readings surrounding the 2014-2018 peak.
Shown is the peak value for the Leominster AURN monitoring station spanning
2014-2018, recorded at 08:00 on 03/12/2014. Presented alongside the peak value
is the 24-hour window surrounding the peak, along with the annual and 24-hour
limit averages.

(b) Leominster 2014 air pollution readings. Presented are all ambient air
pollution measurements for NO2 at the Leominster for the year 2014. Emphasized
is the peak value, which is further examined in Figure 1(a), along with the
annual and 24-hour average limits.

Figure 1: Leominster AURN monitoring station NO2 measurements. Figure 1(a)
shows how the peak air pollution reading for NO2 at the Leominster station
dramatically exceeds the 24-hour limit, even more so for the annual limit,
showing how there can be periods of quite extreme pollution in the context of
the annual limits. Figure 1(b) shows how there can be extended periods where
the air pollution levels are below and exceed the designated limits and the
relation of the monitoring station peak to all available data for the station
in 2014.

##  2 Related Work

###  2.1 Measuring Air Pollution

Various methods exist to offer insight into air pollution concentrations in a
given location. The most robust and straightforward method available involves
specialized equipment designed to provide direct measurements of air pollution
concentrations. In-situ measuring equipment can be broadly categorized into
two groups: high-quality stationary monitoring stations discussed in Section 1
and more mobile low-cost air quality sensors [14].

While high-quality fixed monitoring stations provide a reliable method of
obtaining air pollution concentration data, their deployment on a large scale
is prohibitively expensive. In 2018, the UK had 165 high-quality monitoring
stations online across the country within its premier monitoring network.
Notably, a majority of these monitoring stations are situated in urban areas,
comprising 144 urban and 21 rural stations. The strategic decision of where to
position these monitoring stations carries the potential to exacerbate
inequality between urban and rural areas, potentially fostering a divide
between rural and urban communities in terms of insight into air pollution
where they live and work [15], particularly when considering that O3 air
pollution can often be worse in rural locations [16, 17].

The emergence of low-cost sensors has made it possible to monitor air
pollution concentrations over a larger geographic area. However, we see two
critical problems with low-cost sensors. One such issue is the quality of the
sensors themselves, which can be influenced by changes in atmospheric
composition and meteorological conditions, or provide false signals if other
air pollutants are present in high concentrations [18]. Another issue is the
quality control that is conducted on the sensors, such as the calibration
checks that go into ensuring that the measurement is made under the same
conditions, such as the height at which the measurement is taken, affecting
the reading that is produced, potentially making comparisons between different
low-cost sensors and even the same sensor between locations more challenging
[19]. There is research being conducted to help combat the issues facing low-
cost sensors; it is still an open challenge but rapidly improving [20]. For
now, low-cost air pollution sensors are only suitable for raising awareness
rather than applications requiring higher accuracy, such as epidemiological
studies or compliance with air quality legislation [21].

An ex-situ indirect measurement of air pollution concentrations can be
achieved with remote sensing. Sentinel 5P [22] is an ESA satellite platform
that can provide insight into air pollution concentrations at a vast spatial
extent. However, a major challenge associated with the use of Sentinel 5P is
the issue of data completeness. Two primary drivers contribute to missing data
from the Sentinel 5P platform. The first challenge arises from the platform’s
orbit, which follows a near-polar, sun-synchronous path [23]. This orbit
causes the platform to consistently pass over a region at a similar time each
day. While this characteristic is advantageous for comparing locations, it
complicates the provision of insight into air pollution concentrations across
an entire day. As a result, questions such as the difference between rush hour
and midnight air pollution concentrations become difficult to answer. Another
factor contributing to data gaps is environmental conditions that may lead to
a specific reading not passing quality control, resulting in missing
measurements on certain days [24]. Another remote sensing platform is the
recently operational TEMPO [25], which provides hourly air pollution
concentration measurements. However, TEMPO shares similar limitations with
Sentinel 5P and only offers coverage over North America. While remote sensing
is a valuable tool in certain circumstances, it cannot provide a complete
picture of air pollution concentrations. This comprehensive understanding is
crucial for designing effective interventions to tackle air pollution.

###  2.2 Modelling Air Pollution

As it is evident that all methods of measuring air pollution concentrations
have drawbacks, models have been extensively utilized to complement real-world
observations. Various types of model frameworks exist, each offering distinct
benefits and drawbacks.

There are two widely used air pollution model frameworks: Lagrangian and
Eulerian. Lagrangian models track individual air parcels (or particles) moving
through the atmosphere. Each parcel is associated with a set of equations of
motion, making the parcel the focal point of the model as it moves through
space and time [26]. On the other hand, Eulerian models do not concentrate on
a single air parcel; instead, they divide the atmosphere into regions, using
fixed points or cells to represent specific locations. The goal is to
understand the concentrations of air pollutants at these specific locations at
different times [27]. Lagrangian models are particularly well-suited for
studying problems where a specific pollution source is of interest, such as
the ash emitted from a volcano [28].

On the other hand, an Eulerian model is well-suited for studying the spatial
distribution and long-term trends of air pollutants, albeit at the expense of
not being able to provide specific information about a given source and
pollutant. Consequently, this work will primarily focus on Eulerian models, as
they provide the necessary data for conducting the analyses and assessments
discussed in Section 1.

Statistical Eulerian models, such as Land use regression (LUR), exist as a
method for creating stochastic air pollution models. LUR incorporates a
variety of predictors, including meteorological, terrain, land use, and road
network data [29]. Another paradigm of Eulerian air pollution models is
represented by mechanistic models, such as GEOS-Chem [30]. These models are
open source and available for use, providing comprehensive spatial coverage of
air pollution concentrations. However, they demand a high level of expertise
in the domain field for interrogation due to their complexity. Additionally,
these models come with extensive requirements for supporting infrastructure,
with a GEOS-Chem $None$x$None$ degree standard simulation requiring 15GB of
RAM444https://geos-chem.readthedocs.io/en/stable/gcc-
guide/01-startup/memory.html.

A rapidly emerging area is the use of deterministic models to address the
current gap within the existing suite of models, providing high-resolution air
pollution concentration data both temporally and spatially; this empowers
stakeholders to make informed decisions concerning air pollution. Several
models in this category are based on data-driven supervised machine learning,
where a target vector, typically representing air pollution concentrations, is
estimated from a feature vector, such as meteorological variables (e.g., wind
speed). The model’s objective is to learn the relationship between the target
and feature vectors in situations where both are available, enabling
subsequent predictions of target vectors when only the feature vector is
available. In the scientific literature, numerous studies utilize machine
learning techniques to forecast air pollution concentrations [31, 32, 33].
However, a limitation exists, as this approach requires air pollution
concentration data from the location being predicted before the time
predicted. Therefore, there is a need for historical air pollution data to be
available. For example, a forecasting model will use air pollution
concentration data from T-X to estimate air pollution at time T, where X is
some defined time, such as 1/3/9 hours. If historical air pollution
concentrations are used, it restricts the method’s applicability to locations
where an air pollution monitoring station exists.

Existing studies have tackled the problem of estimating air pollution
concentrations in locations without monitoring stations. However, the studies
focus either on small geographical areas, such as the Bay of Algeciras (Spain)
with hourly temporal resolution [34] or a large geographical area with low
temporal resolution, such as monthly [35]. Some work has been able to achieve
higher spatial coverage with daily temporal resolution [36, 37]. As such, this
work presents a model combining these aspects, predicting hourly temporal
resolution concentrations across England’s large geographical area, a
considerable challenge due to the variance of air pollution concentrations in
locations covered.

This manuscript aims to use machine learning to produce data similar to an
Eulerian model framework. While traditionally, the concentration is resolved
over an area in an Eulerian model, the model presented here can be considered
an approach of using machine learning as a synthetic monitoring station. The
process that is followed for the model is answering the question of the air
pollution concentration reading of a monitoring station that experiences the
environmental conditions described by the input data. The model takes the
training data to learn the relationship between environmental conditions and
air pollution, allowing us to use the environmental conditions that are known
in all locations across the England and make predictions of air pollution
concentrations that are not so readily available, providing a complete picture
of air pollution concentrations in the England. Compared with other
deterministic methods, such as mechanistic models, a key benefit of the
approach is the improvement in computational speed. In contrast, more
traditional Eulerian models involve spatial dependencies between grids, where,
for example, two adjacent grids impact each other. The framework presented in
this work, however, treats each synthetic monitoring station as independent
from one another. This approach offers a significant speedup in computation
through the parallelization of predictions, while also enabling more
accessible exploration of data by predicting air pollution locations
independently. This novel approach is a key contribution of this work,
utilizing machine learning to underpin a scalable estimation of ambient air
pollution concentrations. Importantly, this approach is linearly scalable
concerning computational complexity, allowing stakeholders to employ a model
capable of predicting air pollution concentrations at any spatial and temporal
resolution.

##  3 Data

The data-driven supervised machine learning model this paper proposes for air
pollution concentration prediction is based on two primary sets of data:
feature vectors and target vectors. In the case of air pollution concentration
estimation, the target vector is the air pollution concentration itself, the
data to be estimated, and the feature vector represents the data used to make
predictions, for example, the wind speed. The model aims to understand the
relationship between the feature and target vectors, e.g., what is the given
NOx concentration at given wind speeds?

###  3.1 Target Vector: Air Pollution Concentrations

We obtained air pollution data for our study from the UK Automatic Urban and
Rural Network (AURN) using the OpenAir package [38]. Our study focuses on
seven pollutants: Nitrogen Oxide (NO), Nitrogen Dioxide (NO${}_{\text{2}}$),
Nitrogen Oxides (NOx), Particles < 10$\mathrm{\SIUnitSymbolMicro m}$ (PM10),
Particles < 2.5$\mathrm{\SIUnitSymbolMicro m}$ (PM2.5), Ozone
(O${}_{\text{3}}$), and Sulphur Dioxide (SO${}_{\text{2}}$). All air
pollutants are measured in micrograms per cubic meter
($\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$). All types
of monitoring stations were included in the study. The number of station types
per pollutant varied, resulting in different data point distributions, as
shown in Table 1, with apparent gaps in some locations for certain air
pollutants, such as Suburban Industrial for PM10, PM2.5, and SO2. Clear
spatial differences exist in the locations of monitoring stations. Figure
LABEL:S-fig:ukAURNNetwork shows the spatial distribution of all AURN
monitoring stations used in this study across three high-level environmental
area classifications.

Each environmental area has a representative area over which the station’s
measurements relate [39]. Urban stations are defined as representative of a
few square kilometres (km2), suburban stations cover tens of square
kilometres, and rural stations encompass at least 1,000 square kilometres.
Each station within the network also has a location type that specifies the
primary source of air pollution at the station. Background stations are
strategically located to ensure that no single source or street significantly
influences the readings at the station. Instead, the measurements reflect an
integrated contribution from all sources upwind. Traffic stations are located
so that the measurements represent a street segment of at least 100 meters,
and industrial stations have a representative area of 250 square meters (m2).

The monitoring station location was abstracted to the closest grid centroid
for ease of creating the needed datasets. Consequently, there is some distance
between the true location and the location where we created the feature vector
for the monitoring station. This abstraction of location provided a common
framework, reducing the computation required to build the associated feature
and target vectors, and facilitating a more straightforward interpretation of
the framework. While the AURN monitoring station guidelines specify a minimal
representative sample area, and the maximum abstraction distance across the
monitoring network locations was 399 meters for the London Hackney monitoring
station, we deemed this to be a worthwhile tradeoff. Full details of the
abstraction distance can be found in Supplementary Material Table
LABEL:S-tab:AURNStationDistanceFromCentroidOverall. It is noteworthy that as
the spatial resolution increases, the associated errors will decrease, leading
to an improvement in the approach. Eventually, the error from abstracting the
location of the monitoring station will be eliminated when the abstracted
distance is below the monitoring station representative sample area. However,
this comes at the cost of considerable additional computational expenses.
Therefore, the experiments in this study represent a lower bound for the
framework’s performance, as any operational deployment could utilize increased
spatial resolution for potential performance improvement.

For the study, we used the years 2014-2016 as the training set, 2017 as the
validation set, and 2018 as the test set. To be included in the study, a
station needed to have at least one measurement in each of these sets.

We conducted preprocessing on the collected air pollution concentration data.
While UK-AIR performs some data validation [40], we undertook additional
preprocessing steps. The initial step involved removing negative values, which
are possible in the UK-AIR dataset [41]. The number of observations removed
per air pollutant due to the presence of negative concentrations is detailed
in Table LABEL:S-tab:aurnNegativeValues. The distribution of the positive air
pollution concentration values can vary widely across air pollutants and
exhibit apparent differences between different environmental locations of
monitoring stations. This variability raises the question of how to identify
outliers, alongside the issue of developing a model that can handle target
vectors with stark differences in distribution. For example, O3 is the only
air pollutant with a non-zero-inflated distribution, with the skewing between
distributions for different environmental classifications for each air
pollutant showing varying degrees of skew, as depicted in KDE plots in Figure
LABEL:S-fig:airPollutionKDE.

Points that are distant from the mean have the potential not to be genuine
outliers, but rather data points generated by different phenomena compared
with the other data points in the distribution. We aimed to identify and
remove both outliers and anomalies within the dataset. The challenge in
identifying outliers lies in the context of the dataset, where a single urban
traffic data point within the context of rural background monitoring station
data points might be flagged as an outlier using established methods like IQR
[42]. We were also cognizant of the potential presence of anomalies in the
dataset. We recognized that a single localized event could drive a high-value
concentration data point. While this reading might be accurate, it does not
align with the AURN monitoring stations’ purpose, where concentrations are
intended to represent a larger geographic area. Consequently, we considered
identifying and removing these values beyond the scope of this work and
proceeded with the study, acknowledging the presence of outliers and anomalous
observations within the dataset that we could not explicitly identify. As
such, we decided not to remove any such data points from the dataset.

Pollutant Name |  Urban Background |  Urban Traffic |  Rural Background |  Suburban Background |  Urban Industrial |  Suburban Industrial
---|---|---|---|---|---|---
NOx | $40$ | $41$ | $11$ | $3$ | $6$ | $2$
NO2 | $40$ | $41$ | $11$ | $3$ | $6$ | $2$
NO | $40$ | $41$ | $11$ | $3$ | $6$ | $2$
O3 | $32$ | $3$ | $13$ | $2$ | $3$ | $1$
PM10 | $17$ | $22$ | $2$ | $0$ | $5$ | $0$
PM25 | $30$ | $15$ | $2$ | $2$ | $4$ | $0$
SO2 | $9$ | $1$ | $5$ | $0$ | $3$ | $0$

Table 1: AURN monitoring station counts by environmental classification per
air pollutant.  The number of stations for each pollutant within the UK AURN
network within England is shown. It can be seen that there is an unequal
distribution across the different environment types, alongside some pollutants
such as SO2, missing some environmental types completely.

###  3.2 Feature Vectors

The data considered in this study can be categorized into different dataset
families, each containing a set of distinct but related datasets describing a
phenomenon associated with air pollution concentrations. Addressing the
temporal and spatial resolution differences between the datasets was a key
challenge in creating a consistent feature vector to estimate the air
pollution target vector. The common framework employed consisted of 355,827
1km2 grids covering the extent of the England land mass. England was chosen as
the study area since it was a common geographical region in all the datasets
examined during this study, as illustrated in Figure
LABEL:S-fig:englandLandGrids. For the study, seven different dataset families
were used, each providing a set of datasets describing a range of related
phenomena that correlate with air pollution concentrations. Across all dataset
families, there are 152 feature vector elements, with Figure 2 showing example
feature vectors across England for each dataset family.

Figure 2: Example feature vector dataset from each dataset family. From left
to right, the example datasets are the majority land use classification for
each grid (geographic family, discussed in Section LABEL:S-sec:landUse),
Sentinel 5P NO2 measurements (remote sensing family, discussed in Section
LABEL:S-sec:remoteSensingData), 100m U component of wind (meteorological
family, discussed in Section LABEL:S-sec:DataDetails:metrological), NAEI SNAP
sector 7 (road transport) NOx emissions (emissions family, discussed in
Section LABEL:S-sec:Datadetails:emissions), road infrastructure distance from
the nearest motorway and total length of residential road per grid (transport
infrastructure structural properties family, discussed in Section
LABEL:S-sec:Datadetails:TransportDataInfratsurtcureStructural), and the car
and taxis score (transport use family, discussed in Section
LABEL:S-sec:transportInfrastructureUseData).

#### Transport Infrastructure Structural Properties, 28 features.

Transport Infrastructure has been shown to provide information concerning air
pollution concentrations [43]. We used Open Street Maps [44] to create annual
snapshots of 14 transport infrastructure networks, from motorways555Open
Street Maps Motorway Highway Classification. to residential666Open Street Maps
Motorway Residential Classification. roads. Using the road network, we
calculated two sets of feature vectors. One detailing the distance to each
road type from the grid centroid, and the second detailing the total length of
the given road type within the grid. Further details on the process conducted
can be seen in Section
LABEL:S-sec:Datadetails:TransportDataInfratsurtcureStructural.

#### Transport Infrastructure Use, 5 features.

Vehicles themselves are a primary driver of air pollution through multiple
processes. Road vehicles exhaust gas air pollutants such as NOx and SO2 [45]
alongside causing PM air pollution [46]. Further vehicles can cause air
pollution through traffic resuspension [47] and fuel spillage and evaporation
[48]. Within England, only traffic counts from point locations detailing daily
traffic flows across different road types for key vehicle types such as Cars
and Heavy Goods vehicles (HGVs) were available from the Department of
Transportation [49]. Using OpenStreetMaps, we created a spatially complete
dataset by providing the average daily traffic flow by road type per meter
across the UK. We then used a spatial microsimulation using data from the UK
Census (providing sociodemographic details of different regions of the UK)
[50] and the UK Time Use Survey (providing details of how different
sociodemographic groups travel, both temporally and by which transportation
mode) [51] to spatially distribute the daily traffic counts to produce an
hourly spatially complete dataset of traffic counts. Complete details of the
process are covered in Section LABEL:S-sec:transportInfrastructureUseData.

#### Meteorology, 11 features.

Meteorological phenomena play a pivotal role in air pollution concentrations.
Wind advects air pollution both to and from locations of interest through
horizontal transport [52, 53]. Temperature can have a range of impacts on air
pollution, impacting temperature inversions [54], the production of O3 air
pollution [55] and the removal of air pollution by plants [56]. UV radiation
directly impacts O3 production [57]. The removal of air pollution from the
atmosphere via deposition by precipitation is notable [58], alongside wash-off
from surfaces [59, 60]. Pressure can also influence air pollution
concentrations, either by vertical mixing in low-pressure systems [61] or
high-pressure systems, causing an accumulation of air pollution concentrations
near the ground through a lack of vertical mixing and advection [62]. Further,
O3 production is increased at higher pressures [63]. The boundary layer also
influences air pollution concentrations through vertical mixing within the
layer. Larger boundary layer heights tend to produce less concentrated air
pollution at the surface. For smaller boundary layer heights, the inverse is
true. [64, 65, 66]. We retrieved data from the ECMWF Re-Analysis Version 5
(ERA5) dataset [67]. ERA5 is a global dataset that details the environmental
conditions at equal space points, at 0.25∘x0.25∘ hourly resolution. We choose
the variables commonly associated with air pollution concentrations in the
scientific literature. We interpolated across the study area to provide a
value for each 1km2 grid centroid; the details of the process of creating the
dataset can be seen in Section LABEL:S-sec:DataDetails:metrological.

#### Remote Sensing, 5 features.

While there are limitations to the data produced by remote sensing, as
discussed in Section 2.1, they provide valuable insight into air pollution
concentrations between locations. We used monthly averages of Sentinel 5P data
[22] to ensure that all locations had a measurement value. Further details of
the feature vector are discussed in Section LABEL:S-sec:remoteSensingData.

#### Emissions, 77 features.

Emissions of air pollutants are the primary driver of a wide range of air
pollutant concentrations. The emissions are classified into 11 SNAP (Selected
Nomenclature for Air Pollutant) sectors denoting the emissions source, with
particular details discussed in Section LABEL:S-sec:Datadetails:emissions. The
first sector "Combustion Energy Production and Transformation" (SNAP 1)
includes power generation which can produce air pollutants such as SO2 [68,
69]. Road vehicles exhaust gas air pollutants such as CO, CO2, NOx, SO2 [45]
and are included in the "Road Transport" category (SNAP 7). SNAP 8, "Other
Transport and Mobile Machinery" includes shipping which emits NOx, PM, CO2 and
VOCs [70], particularly SOx from the marine fuels which has a high sulfur
content [71]. Organic waste in landfills ("Waste Treatment and Disposal", SNAP
9) can produce VOCs, a precursor to O3 [72]. Agriculture emissions (part of
SNAP 10, "Agriculture, Forestry and Land Use Change"), comprise a large source
of air pollutants, for example 39% of global PM2.5 is caused by ammonia from
livestock manure and urine and synthetic nitrogen fertilisers [73]. Other
emission sectors are "Combustion in Commercial, Institutional, Residential and
Agriculture" (SNAP 2), "Combustion in Industry" (SNAP 3), "Production
Processes" (SNAP 4), "Extraction and Distribution of Fossil Fuels" (SNAP 5),
"Solvent Use" (SNAP 6) and "Nature" (SNAP 11).

#### Land Use, 22 features.

The land use composition of a given area is related to air pollution
concentrations, such as throughout greenspace [74, 75] and urbanisation [76,
77]. Land use composition profiles were created for each grid using the UKCEH
25m Land Cover Maps [78]. Details of the process and the different land use
types are discussed in Section LABEL:S-sec:landUse.

#### Temporal Aspects, 4 features.

Air pollution displays various temporal cyclical elements, including diurnal
cycles caused by rush hour for NO2 [79], UV radiation for O3 [80], and
boundary layer height for all pollutants [81]. Weekly trends also emerge due
to the working week affecting transportation and industrial emissions for NOx
[82], with similar patterns observed for PM [83]. Seasonal cycles for PM are
evident due to winter residential heating [84], with similar factors
contributing to an increase in SO2 [85]. Furthermore, winter has a higher
probability of adverse meteorological conditions, which reduces vertical
mixing [86]. Additionally, colder temperatures and reduced sunlight in winter
months affect O3 production [53]. Consequently, the hour, day of the week,
week number, and month were all included as elements in the feature vector.

The following section justifies the inclusion of such a broad range of
different datasets by exploring the relationship between the feature vector
and the air pollution concentrations at AURN monitoring stations. This
analysis highlights the differences between types of air pollution and
variations experienced at different environmental types for a single type of
air pollution, ensuring robust estimation of air pollution concentrations
across all environment types found in England.

##  4 Feature Selection

###  4.1 Air Pollution and Feature Vectors

Air pollution can be attributed to various sources, with different processes
influencing its concentration, as discussed in Section 3.2. The issue of
different drivers of air pollution is further complicated when considering
that at different locations, the key phenomena driving the pollution
concentrations are different, as detailed within the AURN environment
classification discussed in Section 3.1. This tangled web of sources, such as
road transport, and sinks, such as wind speed, makes it challenging to
identify a common set of datasets that can provide insight into all the air
pollutants of interest. Therefore, this section aims to untangle the
relationship between the different air pollutants and monitoring station
environment types to provide insight into the different feature vectors and
their relationship with the air pollutant measurements. This provides insight
into the benefit of each dataset, detailed in Section 3.2.

Figure 3 shows the average Spearman correlation coefficient across all
monitoring stations for NOx and O3. These figures depict the ten highest
magnitude feature vector elements in both directions. The differences in the
contributing sources of air pollution for a given pollutant are evident and
appear to support the scientific literature regarding the relationship between
different air pollutants and their sources and sinks.

For example, NOx has the highest positive Spearman correlation coefficient
with the emissions dataset, particularly SNAP sectors 1 and 2, indicating a
strong relationship between energy production and transformation emissions and
the commercial, institutional, residential, and agriculture sectors. It is
also notable that the highest magnitude negative Spearman correlation belongs
to the sinks, namely wind speed and the boundary layer height, as expected.

In contrast, O3 presents a very different situation compared to NOx; the
highest magnitude correlation is inverse. Both wind speed and boundary layer
height have a high positive magnitude Spearman correlation, highlighting that
the same phenomenon can have a completely opposite relationship on the
concentrations of air pollutants. It also follows that the correlation between
“Downward UV Radiation At Surface” and O3 has a positive correlation, given
that O3 is produced under sunlight by the precursors of NOx and Volatile
Organic Compounds (VOCs) [87], highlighting that more sunlight results in more
O3 being produced.

Figure 4 illustrates the relationship between feature vectors for Rural
Background and Urban Traffic monitoring station environment classifications
(discussed in Section 3.1) for the air pollutant NOx. Each subclassification
of monitoring stations indicates the primary contributor to the measured air
pollution. Background stations have no single primary source, while traffic
stations are primarily driven by traffic.

For the Urban Traffic station, the strongest positive correlation across air
pollutants is with SNAP Sector 7, denoting road transport emissions. Notably,
there is a strong relationship with SNAP Sector 6 NMVOCs, indicating solvent
use for NMVOCs. This might be explained by the small number of data points for
monitoring stations (41 Urban Traffic stations), alongside emissions data
being based on extensive scaling depending on the hour, week, and month of
interest [88]. The potential for confounding variables, such as NMVOC
emissions arising from vapor from petrol [88], makes the 0.014 magnitude
difference in relationship strength negligible. The current data quality is
suitable for identifying general trends rather than pinpointing the most
substantial relationship by sector.

The transport use dataset exhibits a positive but weaker Spearman correlation
with the Urban Traffic site, with an average score of 0.36 across the five
datasets. In contrast, the Rural Background sites for the transport use
datasets have an average of 0.05. This aligns with the literature, showing a
clear signal for increased traffic near an Urban Traffic monitoring site and
increased NOx concentrations. It also agrees with the AURN environment
classification, with a still positive but significantly reduced magnitude
correlation.

As the AURN environment classification is based on the primary emitters
closest to the station, it then follows that both station types have the same
feature vector for the highest negative magnitude Spearman correlation with
the expected boundary layer height and wind, namely the meteorological
variables that are present across all monitoring stations, and the sinks in
the case of NOx.

Figure 3: Spearman correlation coefficients overall mean for all pollutants.
The mean Spearman correlation coefficients for NOx and O3 across all the
environmental classifications of the AURN network for the ten most extreme,
both positive and negative, for the feature vectors are shown. The sources and
sinks of the air pollutants are different, aligning with the scientific
literature (Section 3.2), with NOx being highly positively correlated with
emission features, whereas O3 exhibits such a relationship mainly with
meteorological features, such as wind gusts. Regarding negative correlations,
the two air pollutants exhibit counter relationships, with NOx having a
negative correlation with the meteorological. The analysis highlights how the
relationships between a particular phenomenon and a given air pollutant can be
widely different in strength.

Figure 4: Spearman correlation coefficients for NOx monitoring station
environmental subclassification locations, Rural Background and Urban Traffic.
While Figure 3 highlights the difference between phenomena and air pollutants,
there exists a further difference between environmental subclassifications.
For the Urban Traffic monitoring stations, it can be seen that the primary
positive correlations are related to road transport as would be expected (the
strong relationship with Solvent Use is likely an artefact of the scaling
performed and discussed in Section 3.2 and LABEL:S-sec:Datadetails:emissions,
alongside a limited sample size of 41 stations). In contrast, the Rural
Background monitoring stations show a strong relationship with emissions from
the residential sector, highlighting that the sources and sinks for an air
pollutant depend on the air pollutant itself and the location of interest.

###  4.2 Inter Feature Vectors

While there is considerable existing literature about the relationship between
different air pollutants and the phenomena covered in the datasets used in
this study, considerably less literature covers the relationship between the
phenomena comprising the feature vector. This section aims to understand the
relationship between the different feature vectors to address the issue of
multicollinearity, which can have significant implications for the machine
learning approach implemented.

The Spearman correlation coefficient was again used to calculate the
relationship between each pair of feature vectors. Figure 5 is a heatmap
representing the Spearman correlation coefficient value for every pairing.
There are no air pollution monitoring stations for some feature vectors—nine
feature vectors for the emissions dataset family and four in the geographic
dataset family. The lack of target vector data at some key feature vector
locations presents the first significant problem with any model developed: not
all environmental condition types experienced within England have
observations. For example, there are no air pollution concentration data for
the Land Use classification of Saltwater, representing a scenario in which the
model has no exposure.

From the heatmap in Figure 5, it is clear that multicollinearity is present
between some features, for example between different species within the same
emissions sector. Two main concerns were identified with the complete set of
feature vectors: the implications on model interpretation depending on the
model chosen and the redundant information between features. The model
interpretation would impact future stakeholder engagement, and redundant
features would make the hyperparameter search more complex. Therefore, we
considered removing features entirely or creating a new set of features
through dimensionality reduction. However, as stakeholder engagement with the
model was an essential aspect of any operational model, and losing the
semantic meaning of features would be detrimental to model interpretation, we
decided against creating a new set of features.

Figure 5: Spearman correlation heatmap between all feature vectors. The grey
lines throughout the heatmap show the data points missing from the dataset,
phenomena with no monitoring stations across all pollutants, including four
geographic features and nine emissions features.

Figure 6: Dendrogram depicting hierarchical clustering of feature vectors. The
lower the linkage distance between feature vectors, the more correlated the
features are, indicating that they provide similar information. Table
LABEL:S-tab:linkageDistance details the number of clusters for different
linkage distances.

Hierarchical clustering was performed between the Spearman correlation of the
feature vectors, allowing us to create a more complex method of grouping
together feature vectors. Figure 6 shows the clustering results. The linkage
distance provides a consistent metric across all feature vectors to explore
the similarity of features and provide clusters of features depending on the
value of the linkage distance provided. Figure 6 shows how related some of the
feature vectors are; for example, the 100m and 10m components of wind in both
directions are highly correlated and therefore have a very low linkage
distance. There are also more complex relations between the feature vectors,
such as within the transport use datasets. Still, there are differences within
the data set, such as car and taxi and bus and coach being highly related but
not to the same degree as HGVs. The motivation for performing hierarchical
clustering is to allow for a subset of features to be selected that provide
the same information as one another, aiding model interpretation. In the case
above, the idea is that including the 100m U component of wind provides the
same information as the 10m U component of wind, so there isn’t a need to
include both.

Table LABEL:S-tab:linkageDistance shows the number of clusters at varying
linkage distances, where increasing the linkage distance results in fewer
clusters as the information provided between datasets isn’t required to be as
strong. As including redundant feature vectors increases the computation costs
of creating and using the model rather than impacting the performance of the
predictions, we decided to keep all feature vectors when training the model.
The intention is to provide a baseline performance of a machine learning model
that utilises all the datasets covered while allowing for an understanding by
individual stakeholders of the redundant feature through the hierarchical
clustering performed, allowing them to subset the datasets as desired for
their particular use case. Using all the feature vectors does mean, however,
that a machine learning approach that is robust to multicollinearity needs to
be chosen. The second issue of model interpretability implications is
discussed in Section 5.1.

##  5 Modelling

Section 5 starts by describing the reasoning behind different modelling
choices. The model’s performance in two critical scenarios is then explored:
forecasting and estimating missing stations. Forecasting aims to answer the
question of, given a location the model has already seen, how well the model
performs when estimating a future year it has not. The estimating of a missing
station then experiments with understanding model performance when predicting
air pollution at a location it has never seen before. The model’s performance
on peak concentrations is also analysed, a critical situation for the model to
perform well. Finally, a justification for including such a wide range of
datasets is motivated by experimenting with a model that predicts based on one
dataset family.

###  5.1 Model Design and Training

The first consideration when choosing the model framework was the need for the
model to be robust to multiple uninformative and redundant features. As
discussed in Sections 4.1 and 4.2, uninformative features exist for various
reasons. Some features are uninformative for specific air pollutants, such as
the transport use features with O3. Features can also be uninformative for
specific environmental locations of monitoring stations, as seen in Section
4.1 for rural background NOx stations. Multicollinearity further compounds the
issue, allowing multiple features to extract the same information about air
pollution measurements, as discussed in Section 4.2. The second consideration
when choosing the model was for it to be robust to outliers and anomalies, as
discussed in Section 3.1. The model we chose to use was LightGBM, a gradient-
boosting algorithm based on decision trees.

LightGBM [89] was identified as a machine learning approach that could address
the concerns raised in the study through various techniques while also
providing state-of-the-art performance on tabular prediction problems.
LightGBM is a gradient-boosting decision tree (GBDT) algorithm where an
ensemble of decision trees is trained in sequence, with the n+1 decision tree
fitting the residuals of the first n decision trees, learning the difference
between the actual target vector and the weighted sum of predictions of the
first n decision trees.

LightGBM allows us to mitigate the impact of uninformative and redundant
features on training time through the tree-building algorithm. The approach
that LightGBM takes when building the decision trees is to split observations
based on the feature vector values, looking for the best possible split
regarding information gain and reducing the uncertainty regarding the target
vector. This involves grouping homogenous instances of data points, such as
instances where there is high transport use at a monitoring station that is
measuring high concentration readings for NOx.

One of the core issues with our air pollution training data is that many data
points within the datasets repeat the same information due to the cyclical
nature of air pollution measurements, causing a considerable amount of bloat
in the datasets. The standard approach to identifying split points within a
GBDT is the pre-sorted algorithm where all possible split points are explored,
an approach which, in this use case, would be highly costly regarding
computation and memory. LightGBM helps tackle this issue by using histograms
when performing the splits, where continuous variables are put into discrete
bins, changing the computational cost from being dependent on the number of
data points to the number of discrete bins created.

The second concern identified when exploring the datasets was the presence of
outliers and anomalies within the dataset. LightGBM inherently tackles this
problem via decision trees being the underlying learner within the model. The
decision tree’s goal is to group homogenous data instances, and there is an
ability to set a minimum number of data instances that comprise a valid leaf
on the decision tree via the “minimum data in leaf” model parameter.

The “minimum data in leaf” parameter allows for a minimum threshold of
homogeneous data points for the LightGBM algorithm to view as a set of data
points that should be learned from and used in predictions. Concerning the air
pollution prediction problem as a notional example, say there is a high wind
speed and low traffic count but a high air pollution concentration reading,
which only occurs once in the dataset; LightGBM will not create a leaf for
this data instance. The scenario described could plausibly happen if a single
air pollution emitter passes by the station, causing an artificially high
measurement that would not represent the geographic area intended for the
station as outlined by the AURN documentation, as discussed in Section 3.1.

There are, however, some trade-offs to the LightGBM solutions presented above.
The feature importances given for the feature vectors via the model will
likely be misleading due to the multicollinearity present. For example, the
most extreme case seen in the hierarchical clustering of multicollinearity is
for the wind speeds at 10m and 100m, where both features exhibit a strong
correlation, meaning that we can extract information about air pollution from
either feature. Therefore, during model building, in the split performed, the
model would use only the 10m or 100m component of the wind direction, as they
would present the same information gain about the target variable as each
other. As the feature importance given by LightGBM is based on the number of
times a feature vector is used, the total number of times the two feature
vectors are used may be split across the two features, reducing the feature
importance given to each one. The feature importances given must be analyzed
considering the clusters presented in Table LABEL:S-tab:linkageDistance,
treating each of the clusters’ feature importances together, or a more
sophisticated technique of model interrogation used, such as SHAP [90].

A key consideration during the model design was the choice of the loss
function. The loss function represents the error of a given prediction, in
this case, quantifying the difference between the prediction and the actual
air pollution concentration measurement of a model, thereby allowing for
comparisons between models and subsequent choice of the optimal model. The
choice of the loss function in this situation was between the mean absolute
error (MAE) and the mean squared error (MSE) [91]. The MAE would help reduce
the influence of higher air pollution measurements on the model present due to
the known presence of outliers and anomalies within the dataset. However,
these high air pollution measurements are of vital interest within the context
of air pollution, even if they are potentially erroneous. So, a tradeoff of
potentially overfitting on these higher values was seen as a worthwhile
tradeoff, and as such, the MSE was chosen as the loss function. The underlying
premise is that
10$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ is more than
twice as bad for human health than
5$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, so using the
MSE is more appropriate given the domain in which the model would be used,
supporting the existing literature that there is a non-linear relationship
between the detrimental effects of air pollution concentrations [92, 93].

As an air pollution concentration can never measure less than zero, we trained
the model on the log transformation of the target vector. We added a small
constant of $1\times 10^{-7}$ to the target vector and then performed the log
transformation due to the presence of 0 concentration measurements within the
dataset. The log transformation and addition ensured that the model would
never predict a negative value as the model output, as the reverse
transformation of calculating the exponential and subtracting $1\times
10^{-7}$ was performed on the output. A further hyperparameter explored during
model training was L2 regularization [94]. Including L2 regularization helps
distribute the weights within the decision tree, encouraging the weights to be
closer to 0 but keeping all feature vectors, ensuring that no single feature
vector drives predictions, which is key with the considerable number of
feature vectors used.

The framework for choosing the model’s hyperparameters was a randomised grid
search of 40 parameter sets. The parameters we optimized during the randomised
search were the L2 regularization and the min data in each leaf already
discussed, alongside the number of leaves, the number of trees, and the max
depth [95]. The number of leaves search space was given the range of 1,000 to
4,095 [96], with the optimal values being chosen near the centre of this
range, validating its choice. The number of trees was controlled via early
stopping, where no additional tree would be added after 30 trees had been
added without any improvement in the loss function performance. Similarly, the
max depth was not limited and left to grow as needed until performance did not
improve during training.

Some model parameters were kept constant throughout the search, such as the
max bin, kept constant at 255. The max bin refers to the number of discrete
bins created for a continuous feature vector. 255 was chosen to ensure that a
range of different splits during model training could be created while also
helping to reduce training time by allowing data to be stored optimally as an
int8 data type. The boosting type used during training was Gradient-based One-
Side Sampling (GOSS) [89]. GOSS is a method of boosting that allows the n+1
decision tree discussed at the start of this section to be trained on a
subsample of the data. The subsample of data chosen is the data that has a
large gradient, i.e. the data has yet to learn well from in the model and a
random sample of the small gradient data, helping to reduce the amount of data
used drastically, and therefore training time. The tradeoff with GOSS is the
potential for overfitting when the datasets are small; however, this was not a
concern in the context of air pollution.

The final consideration was the grouping and number of models to develop. One
possible choice was creating a single unified model with all seven air
pollutants comprising the target vector. However, due to the considerable
imbalance in the number of data points and the issue of every monitoring
station measuring a different subset of pollutants, the number of locations
with every air pollutant measured at the same timestamp was minuscule. Another
possibility was to create an individual model for each environment type
covered in the AURN environment classification, such as Urban Traffic, Rural
Background. However, this approach presented the problem of requiring a
determination of the environment type of every grid in the study where there
wasn’t an existing monitoring station. Therefore, we created a single model
for each of the different air pollutants mixed with all the different
environment types, the benefit of which is simplifying the process of
estimating a never before seen location while making use of all of the air
pollution observations possible.

During the hyperparameter grid search, data from 2014-2016 was used as the
training set, with the validation set being 2017 and 2018 as the test set. We
split the dataset temporally to ensure no data leakage and to give an
intuitive sense of the performance metrics gathered. We chose the best
parameter set based on the model’s MSE on the validation set across the
parameter sets. Subsequently, using the best parameter set to train a model
with both the training and validation set, with performance evaluated using
the test set, data the model has never seen. The R2 score for each model on
the different sets was calculated at each stage.

To allow flexibility in extending the model with new data, we deliberately
excluded feature vector elements that would identify monitoring station
details, such as names or locations. Additionally, we opted not to include
lags of air pollution concentrations, like using the concentration at T-1 to
estimate the concentration at time T. This decision enables us to make
predictions even in the absence of a specific air pollution measurement,
promoting the use of observations as independent entities. This structure
facilitates the tabular format, leveraging the state-of-the-art performance of
LightGBM. The temporal and spatial independence of observations supports the
creation of a lightweight model, conducive to parallel computation for
different locations and time points. Our experiments addressed two crucial
scenarios, providing insights into the model’s temporal and spatial
performance. We assessed its ability to forecast air pollution concentrations
into the future (discussed in Section 5.2), and extended the analysis to
evaluate the model’s performance in estimating air pollution concentrations in
spatial locations not previously encountered (discussed in Section 5.3).

###  5.2 Filling Temporal Missing Data

The initial set of experiments focused on evaluating the models’ capability to
predict future air pollution concentrations at locations already included in
the model. These experiments aimed to assess the model’s performance in
forecasting scenarios. The achieved performance serves as a conservative
estimate for filling in missing data temporally in a given monitoring
station’s time series, considering that air pollution concentration readings
at the estimated time would be available in an operational hindcast situation.

Table 2 presents the R2 score for the model developed at each stage, as
discussed in Section 5.1. The results illustrate a degradation in the model’s
performance as it moves temporally away from the data it was initially
optimized for during the randomised parameter grid search. These experiments
demonstrate that the model parameters identified during the randomised search
remain consistent across the three datasets, with minimal performance loss
observed between the validation and test sets. This observation supports the
idea that the model is effectively learning the true relationship between the
feature and target vectors.

Pollutant Name |  Dataset Train Score |  Dataset Validation Score |  Dataset Test Score
---|---|---|---
NO2 | $0.85$ | $0.77$ | $0.77$
NOx | $0.82$ | $0.75$ | $0.74$
NO | $0.73$ | $0.67$ | $0.65$
O3 | $0.8$ | $0.7$ | $0.67$
SO2 | $0.45$ | $0.43$ | $0.3$
PM10 | $0.51$ | $0.38$ | $0.32$
PM25 | $0.55$ | $0.35$ | $0.29$

Table 2: R2 scores depicting forecasting performance (2014-2016 train score,
2017 validation socre, 2018 test score). The dataset train score shows the
model’s performance in capturing the relationship of the training data shown
with the validation showing the performance in 2017 and the test score on 2018
data. The similar performance between the validation and test scores shows
that the model optimised during the parameter search is learning the true
relationship between the features and air pollution that is robust to data
never seen before.

In the best-case scenario, NO2 shows no drop in performance (rounded to 2
decimal places) between the validation and test sets. The most significant
performance decrease is observed in SO2; however, it’s important to note that
this may be influenced by a data issue, as SO2 has significantly fewer
stations (18) compared to NO2 (103), as detailed in Table 1.

While a benefit of the model presented is the ability to forecast air
pollution concentrations into the future, answering the question of what air
pollution concentrations at a station will look like in the next year, the
adaptable temporal and spatial independence discussed in section 5.1 allows
for the model to be used to estimate missing data. Figure 7 shows the model
used to estimate the missing data in the NO2 observations for the Chesterfield
Loundsley Green monitoring station from 2014-2018. There are two possible
cases for the missing data being filled in. The first is to backdate or
postdate the observations depending on when the station came online or was
decommissioned. Chesterfield Loundsley Green came online on 01/03/2015 [97],
but the model can backdate the observations to 01/01/2014, extending the
readings and helping to create a complete dataset. It is also possible to
extend the life of a station if it was taken offline by filling in
observations since the station was decommissioned [98].

The second situation where the model can fill in missing data is when an issue
at the monitoring station or associated infrastructure causes the station to
go offline and measurements not to be reported [99]. However, one potential
issue with this approach is if there is a particular reason that the site
cannot report data, for example, when wind speeds are over a defined speed.
This situation would indicate that no data is within the training set
concerning this specific situation, indicating the model is extrapolating.
However, this is not a concern in this situation as AURN reports the reasoning
behind data not being reported, such as a communication issue or an instrument
error. It is something, however, to be understood in the context of any future
work that uses this framework where this situation could occur. In Figure 7,
there are three prolonged periods in which Chesterfield was not reporting NOx
measurements. The model presented can fill in these periods alongside the
periods from before the station came online to create a time series for the
station that has all available data as seen in Figure 7 where the real
measurements from the station have been augmented with the model output where
real measurements are not available.

Figure 7: Chesterfield Loundsley Green NO2 concentrations augmented dataset,
with missing AURN measurements filled with model predictions. Figure 7 shows
that the station’s measurements (green) started in early 2015 with three clear
periods of long-term missing data. The model predictions (yellow) can create a
complete augmented time series using the model.

###  5.3 Filling Spatial Missing Data

The second set of experiments that we conducted explored the ability of a
model to be trained and predict the complete time series for another station,
never seen before. We used 5-fold leave-one-out validation (LOOV) to
experiment with this scenario. The results from this experiment provide an
understanding of how the model performs when filling in missing air pollution
concentration data spatially, a situation akin to using the model as synthetic
stations across England at locations where no station has ever existed.

The same experimental design as Section 5.2 was repeated alongside a final
step that calculates the LOOV score for every station not included in the
training, validation or test set. Table 3 shows that the models trained during
the 5-fold LOOV can retain their future predictive performance, with minor
differences for the performance of ai pollutants across the different subsets
of stations used, showing the results from section 5.2 to be robust to
changing input datasets. Table 4 shows the LOOV summary statistics for the
experiments conducted, based on the R2 retrieved from the model estimating the
complete time series of a monitoring station’s data. Four different summary
statistics were considered from the set of LOOV results, namely the mean,
median, min and max results. The max LOOV results are positive for all of the
pollutants, indicating some merit to this approach across all pollutants. This
is further supported by the majority of positive results in the mean and
median LOOV for all pollutants apart from SO2. Of central interest is the LOOV
min, where for all pollutants other than PM${}_{1}0$ there is a negative R2,
indicating a prediction across the time series that is worse than simply
predicting the average concentration. In this case, a potential hypothesis for
the model performance is a lack of data available. The performance of SO2
supports this hypothesis; the worst out of all pollutants and has the least
available data.

Pollutant Name |  Dataset Train Score |  Dataset Validation Score |  Dataset Test Score
---|---|---|---
NO2 | $0.85$ | $0.77$ | $0.77$
NOx | $0.82$ | $0.75$ | $0.74$
NO | $0.73$ | $0.67$ | $0.65$
O3 | $0.81$ | $0.7$ | $0.67$
SO2 | $0.46$ | $0.42$ | $0.29$
PM10 | $0.54$ | $0.38$ | $0.32$
PM25 | $0.58$ | $0.34$ | $0.29$

Table 3: R2 scores depicting forecasting performance for 5-fold leave-one-out-
validation.  The experiment conducted aimed to ensure that with different
subsets of monitoring stations, the forecasting performance of the model
remains robust. Shown with Table 3 having similar performance as the
experiment result shown in Table 2, particularly the test score, data the
models have never seen before.

Through the definition provided for the AURN station environment types, we
know that air pollutant concentrations exhibit different signatures in
different locations. There is the possibility of further subclassifications
within these environment types. For example, taking the Urban Traffic
environment type, there could be a distinction between the Urban Traffic
stations within London and outside of London. Particularly for the approach
used within this work, a data-driven model, this can have wide-ranging
implications. Suppose it does exist that a subset or single station within the
LOOV dataset is unique compared to others, resulting in no similar data
present within the training data. In that case, the model presented will fail
to replicate the time series measured. This hypothesis is supported as the
temporal experiment framework performance is consistent across all the
experiments conducted, shown in Figure 2 and Figure 3, with the issue only
appearing in the spatial experiments. As a more concrete example of this
scenario, the Aston Hill AURN monitoring station is the only monitoring
station in the NOx dataset with no roads nearby, a clearly unique monitoring
station in the dataset. This issue is further complicated when considering the
results from Section 4.2, where not all feature vector elements have an air
pollution monitoring station present, showing clear environment types that
have no data available, denoting situations where the model is extrapolating
and potentially widely wrong.

The experiment here provides the basis for using the model to create a
complete spatial map of pollution across England and Wales with the framework
of synthetic stations. In the case of the grid system used in this study, the
framework acts as if 355,827 synthetic stations are present at the centroid of
each grid, which gives a point sample measurement of the ambient air pollution
concentration at a given time. Figure 8 shows the resulting air pollution
concentration map we can create from the model, predicting air pollution at
every location across England.

Pollutant Name |  Estimation LOOV Max |  Estimation LOOV Min |  Estimation LOOV Mean |  Estimation LOOV Median
---|---|---|---|---
NO | $0.62$ | -2.28 | $0.1$ | $0.21$
NO2 | $0.7$ | -1.75 | $0.25$ | $0.37$
NOx | $0.67$ | -1.46 | $0.2$ | $0.32$
O3 | $0.78$ | -1.95 | $0.45$ | $0.62$
PM10 | $0.59$ | -0.17 | $0.35$ | $0.38$
PM25 | $0.59$ | $0.23$ | $0.45$ | $0.46$
SO2 | $0.1$ | -1.65 | -0.2 | -0.02

Table 4: R2 scores for missing monitoring stations performance summary
statistics for 5-fold leave-one-out-validation. The summary statistics show
the R2 for each monitoring station in the study for a model that has never
seen the stations’ data before. The approach has clear merit, with all air
pollutants having a positive maximum score. However, some monitoring stations
have a negative minimum score, driven by their unique nature concerning the
feature vectors and phenomena driving the air pollution concentrations at a
given location. The mean and median R2 scores show that the approach works for
most stations for most air pollutants in estimating air pollution
concentrations at a missing location.

Figure 8: Full spatial map of England for all pollutants for 8AM on
19/01/2018, chosen arbitrarily as a typical working day away from national
holidays in England. Plotted on a log scale to help highlight the differences
within regions in the map.

###  5.4 Prediction of Peak Values

While the R2 score provides a metric for evaluating the overall performance of
the model on the entire dataset, a critical consideration in the context of
air pollution concentration estimation is the model’s performance during peak
concentrations. Given that peak concentrations have the most significant
impact on human health and well-being and are the focus of policymakers when
designing interventions, it is crucial to assess how well the model performs
in these high-concentration scenarios.

We conducted an analysis of the model predictions during peak concentration
events observed at each station. Figure 9 illustrates the model’s predictions
compared to real-world measurements from AURN monitoring stations.
Specifically, Figure 9(a) focuses on the Leominster monitoring station
discussed in Section 1. The visual comparison reveals that while the model did
not capture the exact magnitude of the peak concentration at the station, it
did exhibit an uptick at the correct time. This raises concerns about the
model’s ability to make high-magnitude predictions. However, further
investigation indicates otherwise, as evidenced by the Stanford-le-Hope
Roadside AURN station, which had a high-magnitude prediction of approximately
140$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ for its
overall peak concentration reading between 2014-2018.

This prompts the question of whether the model’s prediction for the Leominster
peak value was an underprediction or if the peak value itself was an anomalous
reading. The percentage difference for the Leominster monitoring station at
03/12/2014 8 AM was 42.5%. In contrast, the mean peak percentage difference
across all NO2 monitoring stations was 22.1% during the Leominster peak,
considerably lower than the overall NO2 average peak distance of 50.72%. The
considerable difference in expected peak distance suggests that the Leominster
station differed from nearby stations at this time. The performance variation
across different pollutants sheds light on a potential issue with the model
stemming from the training data, as depicted in Table 5. Notably, O3 exhibits
the best performance in predicting peak concentrations. As outlined in Section
2, meteorological conditions predominantly drive O3 concentrations. The ERA5
data used in this study stands out as the most robust dataset, featuring
temporally and spatially unique data points.

In contrast, other datasets used in the model lack this level of uniqueness,
relying on idealized values that may not accurately represent the true
variability. For instance, the transport use dataset family follows a common
temporal distribution, emissions are scaled, and datasets like transport
infrastructure lack sufficient variability. These idealized values present an
avenue for improving the model by seeking enhanced data representations of
phenomena influencing air pollution. Additionally, addressing the potential
impact of outlier and anomalous data points could further enhance model
performance. The peak distance percentage is defined in Equation 1.

$\left(\frac{(\text{Measured Peak}-\text{Model Prediction})}{\text{Peak
Value}}\right)\times 100$ (1)

(a) Leominster NO2.

(b) Stanford-le-Hope Roadside NO2.

Figure 9: Prediction of peak values for NO2 monitoring stations. In Figure
9(a), it is evident that the model failed to capture the peak concentration
for the Leominster monitoring station. However, there is a noticeable uptick
in the concentration prediction at the correct time, raising concerns about a
consistent underestimation by the model. Conversely, Figure 9(b) illustrates
the peak prediction for the Stanford-le-Hope monitoring station. The model not
only captures the peak but also yields a magnitude considerably higher than
that for Leominster, offering an initial indication that the model may not be
systematically underpredicting concentrations.

Pollutant Name |  Average Peak Distance Percentage (% of µg/m3)
---|---
O3 | $32.067,903,047,509,11$
NO2 | $50.724,480,990,402,11$
NOx | $63.815,986,599,087,94$
NO | $72.316,985,910,522,08$
PM25 | $84.212,658,754,837,79$
PM10 | $87.370,114,239,066,2$
SO2 | $93.000,753,225,984,38$

Table 5: Average peak concentrations prediction difference. The peak
percentage difference is calculated according to formula 1. O3 has the best
performance for predicting the peak concentrations across all the monitoring
stations, with SO2 having the worst performance. This ordering presents
further evidence that the likely explanation for the model not capturing the
peak concentrations is not the model framework itself but rather the input
data. SO2 has by a considerable margin the least amount of data across the air
pollutants (Table 1, alongside O3 being most correlated (Section 4.1) and
driven by meteorological phenomena according to the scientific literature
(Section 3.2), which given that ERA5 is the highest quality dataset, with
unique points spatially and temporally indicates that the difference in data
is likely driving the difference in peak concentration estimation performance.

###  5.5 Data Subsetting

The scalability of the framework has been considered primarily within temporal
and spatial resolution dimensions. However, the framework’s adaptability
extends to different amounts of data, contingent on the availability of
specific datasets. Variations in input datasets allow for the development of
models tailored to different use cases. For instance, datasets that are only
available after historical dates, such as remote sensing, may be excluded when
creating models for forecasting purposes. Table
LABEL:S-tab:dataSubsetForecastingSpatial provides experiment results for this
type of model. Additionally, in situations where a location lacks certain
datasets, such as traffic estimates for an entire country, models can be built
using only the available datasets. Both meteorological and remote sensing
datasets have global availability, making them suitable for creating baseline
hindcast models for locations worldwide. Table
LABEL:S-tab:dataSubsetGlobalSpatial showcases the performance of such a model.

Further experimentation delved into assessing the performance of each dataset
family, as depicted in Table 6. These findings support the concepts presented
in Section 4.1, emphasizing that no single dataset alone can achieve a
positive mean LOOV score.

Dataset Family |  Dataset Train Score |  Dataset Validation Score |  Dataset Test Score |  Mean LOOV
---|---|---|---|---
Emissions | $0.46$ | $0.42$ | $0.42$ | -0.23
Geographic | $0.31$ | $0.25$ | $0.29$ | -0.46
Meteorological | $0.15$ | $0.17$ | $0.14$ | -0.53
Remote Sensing | $0.35$ | $0.31$ | $0.36$ | -0.38
Temporal | $0$ | $0.03$ | $0.02$ | -0.64
Transport Infrastructure | $0.32$ | $0.29$ | $0.29$ | -0.41
Transport Use | $0.35$ | $0.23$ | $0.16$ | -0.47

Table 6: Repeat experiments results of Tables 2 and 4 for models trained on
individual dataset families (Section 3.2) for NO2.  The framework presented
can be used on varying amounts of input data, depending on available data,
providing a basic understanding of limitations when moving the model between
areas, such as being used to predict countries other than England. Table 6
shows that in the case of England, while individual dataset families can
forecast into the future, the performance of estimating missing monitoring
stations is limited and requires datasets that cover a wide range of phenomena
to achieve the same performance as 4.

##  6 Research Data Output

As part of our ongoing efforts, we plan to release an open-source dataset
consisting of two components. The first component is the augmented AURN
dataset, as illustrated in Figure 7. This dataset includes model predictions
for air pollution concentrations at all AURN monitoring stations for the
period 2014-2018, which were utilized in this study. The second component is a
comprehensive air pollution concentration map for England, encompassing each
air pollutant for the year 2018. This dataset provides a spatial resolution of
1 km2 and hourly temporal resolution. We anticipate that this dataset will be
of significant interest to various stakeholders, as outlined in Section 1.
Moreover, it opens avenues for the research community to explore a diverse
range of research topics that were previously constrained, given that current
air pollution estimations at this spatial resolution typically operate at the
annual temporal level.

The presented dataset opens up diverse research possibilities, with one
illustrative example being the examination of air pollution concentration
variations across different locations concerning legislation compliance, as
discussed in Section 1. Figure 10(d) showcases a series of heatmaps
representing the grids employed in this study. Each grid is colour-coded based
on the number of times it exceeded specific concentration thresholds in 2018
at an hourly granularity. The maximum possible number of exceedances per grid
is 8760, representing the total hours in 2018.

The thresholds considered include
10$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, aligning
with the WHO NO2 air quality guideline level for the annual temporal period.
The second threshold is
25$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$,
corresponding to the WHO 24-hour aggregate air quality guideline [5]. The
third threshold is
40$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$, reflecting
the UK National Air Quality Guideline annual limit. Lastly,
200$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ represents
the UK National Air Quality Guideline hourly target for NO2 concentrations
[7]. While not all these thresholds directly pertain to hourly concentration
legislation, they provide a comprehensive set of benchmarks derived from
actual legislation, offering insights into the distribution of air pollutants
across England.

The analysis reveals compelling insights into air pollution concentration
exceedances across various thresholds. It was found that 99.96% of locations
surpassed the
10$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold at
least once, 63% exceeded the
25$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold at
least once, 26.2% exceeded the
40$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold at
least once, and only a single grid exceeded the
200$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold
at least once. This analysis serves as a valuable tool to pinpoint locations
demanding further investigation into air pollution concentration causes and
potential interventions at the local level. For instance, the coordinates at
latitude 51.5, longitude -0.15, representing a location exceeding the
200$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold
with concentration predictions surpassing 10 for every hour in 2018,
underscore the need for targeted attention. Moreover, leveraging the temporal
precision of the predictions allows for flexible aggregation to various
temporal levels stipulated in UK or EU legislation. For instance, aggregating
to a 24-hour mean [100] for each grid facilitates a comprehensive assessment
of legislation compliance, as depicted in Figure 11.

(a) 10$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$
threshold.

(b) 25$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$
threshold.

(c) 40$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$
threshold.

(d) 200$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$
threshold. Figure 10: Count of times that a grid exceeded the outlined
thresholds for NO2 in 2018. Figure 10(a) shows the
10$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold
where one grid exceeds the threshold for every hour of the year, with 99.6% of
grids exceeding the threshold at least once. Figure 10(b) depicts the counts
for the 25$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$
where the max count was 8656 exceedances across the year, with 63% of grids
exceeding the threshold at least once. Figure 10(c) uses a threshold of
40$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ where the
max count for exceedances was 8,086 across the year, with 26.2% of grids
exceeding the threshold at least once. Figure 10(d) denotes a
200$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ threshold,
where only a single grid exceeded the threshold twice across the year.
Latitude 51.5, longitude -0.15 was the location that exceeded the threshold, a
central London location with the postcode W1G 6JA.

Figure 11: 24-hour mean [100] exceedance counts example. The threshold used is
a mean of 25 $\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$.
As the hourly level is the most common high-resolution temporal level
mentioned in air quality legislation, pursuing data at this level allows for a
more coarse temporal level to be calculated from the input data, resulting in
the dataset providing complete legislation coverage no matter the resolution
of interest.

##  7 Discussion

We have released two datasets that hold significant value for the scientific
community, policymakers, and the public. The comprehensive spatial dataset
offers valuable insights into locations where air pollution concentration data
might be non-existent at the hourly temporal level. If one were to acquire the
data produced by our model through real-world measurements, the cost would
amount to £70B777Calculated based on 355827 monitoring stations at a cost of
£198000 per station, as outlined in Section 1. through AURN monitoring
stations.

The augmented AURN dataset, generated from this study, provides a complete
temporal perspective of all monitoring station measurements across England.
This is particularly crucial for compliance assessments related to absolute
threshold exceedances, such as NO2, where a detailed limit of
200$\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$ should not
be exceeded more than 18 times a year [7]. Ensuring a complete time series
with measurements at each time step is essential when comparing two locations,
especially in situations where missing data during peak pollution periods,
like NOx during rush hour, could potentially mask crucial information. This
consideration becomes paramount when creating higher temporal resolution
statistics through UK AIR888UK AIR Statistics Using Incomplete Data, denoted
by data capture rate..

While the presented model has demonstrated its effectiveness in filling
missing data from high-quality air quality monitoring stations, its most
significant advantages lie in its potential application to low-cost monitoring
sensors and citizen science initiatives. The symbiotic relationship between
the model and low-cost sensors addresses a core issue present in both
approaches. The model’s performance is notably impacted by a lack of data. In
locations where the installation of more expensive AURN-style stations might
not be deemed a worthwhile investment, low-cost sensors can be strategically
deployed to fill data gaps. These sensors, due to their minimal cost, can be
implemented in various locations, enhancing spatial coverage.

Conversely, the model can contribute to overcoming the challenges associated
with low-cost sensors, which are often less robust than AURN stations. By
leveraging the model, missing data points can be backfilled temporally and
spatially, ensuring the generation of a more complete dataset. This
collaborative approach is particularly valuable in scenarios where the less
frequent deployment of AURN stations results in gaps in the feature vector, as
discussed in Section 4.2. An additional advantage is the model’s ability to
handle temporally messy data commonly encountered in citizen science
initiatives. Unlike AURN monitoring stations that provide data at regular
intervals, citizen science datasets may exhibit irregular time stamps (e.g.,
10:14, 10:47, and 11:46). The model enables a more sophisticated estimation of
air pollution at specific times (e.g., 10:00, 11:00, 12:00), facilitating
further analysis such as legislation compliance or integration with other
forecasting systems.

Importantly, the model method offers substantial benefits compared to other
approaches for filling missing data, such as interpolation, as it takes into
account the nuanced patterns present in air pollution concentration time
series datasets.

Although the proposed model demonstrates significant advantages, there is a
clear pathway to extracting even more benefits from the model framework, given
its inherent scalability in both spatial and temporal dimensions. The
extension of the method to make predictions at a minute temporal-level is
straightforward, and similarly, increasing the spatial resolution grid size to
100m2 is feasible. This scalable approach empowers researchers by providing
the desired data without being constrained by limitations in financial
resources for monitoring station placement.

Furthermore, the encoding of the temporal aspect into a tabular format
facilitates a substantial acceleration through parallelization. Each timestep
and grid within the estimation is independent of one another, enabling the
simultaneous calculation of all timesteps and grids. This approach yields a
significant speedup over traditional forecasting methods, whether machine-
learning or physics-based, that rely on lags from previous timesteps.

From a performance standpoint, the capacity to parallelize estimations becomes
pivotal when combined with the scalability of the approach. This combination
forms the basis for a computationally effective method of estimating air
pollution concentrations at a global level. Future work could extend the
experiment conducted in Section 5.3, where air pollution concentrations at one
station were estimated using data from other monitoring stations, to a study
that analyzes the feasibility of estimating air pollution between countries
and their respective air pollution monitoring networks. The potential benefit
of this analysis is to help reduce inequalities between countries concerning
monitoring stations, enabling the design of interventions based on air
pollution without the need for high-cost, dedicated monitoring station
networks to be implemented by a country’s government.

While the datasets employed in this study successfully estimated air pollution
concentrations under a variety of conditions, there remains room for
improvement in the input feature vectors. Presently, the model does not
consider variations associated with specific days, such as distinct travel
patterns on bank holidays compared to regular weekdays. Incorporating local
knowledge into the model, such as categorizing whether a day is a bank holiday
or another national holiday, would enhance the model’s understanding of unique
circumstances on special days, such as Bonfire Night in the UK, known to have
considerable impact on air pollution concentrations [101]. Additionally, some
feature vectors used in the model will improve over time as technology
advances, enabling improved model performance. For example, remote sensing of
trace gases over Europe will improve with the Sentinel-4 missions, which are
currently scheduled for launch in 2024, on the MTG-s Satellite [102].
Sentinel-4 will provide hourly temporal resolution, with a spatial resolution
of 8km for much of northern Europe for O3, NO2, SO2, and aerosol optical depth
[103].

In addition to incorporating additional knowledge into the model, a thorough
analysis of the training data used in the study is crucial to ensure
comprehensive coverage of all scenarios, minimizing the need for extrapolation
during model estimations. For instance, as discussed in Section 4.2, there are
environmental conditions where no air pollution concentration measurements are
available. Future work could focus on analyzing missing scenarios in the
training data, identifying locations where additional air pollution monitoring
stations should be placed. When combined with low-cost sensors, this approach
could form the basis for a dynamic mobile monitoring network to identify areas
where the model predictions are most uncertain.

In summary, we believe this work holds significant importance for a broad
audience, addressing critical challenges outlined in the United Nations (UN)
Sustainable Development Goals (SDGs). The work presented empowers decision-
makers with high-quality data for crucial indicators (UN SDG 3.9.1, 11.6.2)
for essential goals such as Good Health and Well-being (SDG 3) and Sustainable
Cities and Communities (SDG 11). The contribution to SDG 3 is evident in
reallocating resources from monitoring air pollution to clean air initiatives,
providing estimates in all regions, not just those with monitoring stations.
Simultaneously, the research contributes to SDG 11 by advancing the
understanding of the relationship between urban and rural air pollution.

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