Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels

Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input-output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM-CTM), and some of those input-output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations. © 2013 Elsevier B.V. All rights reserved

Neural network-based metamodelling approach for estimation of air pollutant profiles

University of Technology, Sydney. Faculty of Engineering and Information Technology.The air quality system is a system characterised by non-linear, complex relationships. Among existing air pollutants, the ozone (O3), known as a secondary pollutant gas, involves the most complex chemical reactions in its formation, whereby a number of factors can affect its concentration level. To assess the ozone concentration in a region, a measurement method can be implemented, albeit only at certain points in the region. Thus, a more complicated task is to define the spatial distribution of the ozone level across the region, in which the deterministic air quality model is often used by the authority. Nevertheless, simulation by using a deterministic model typically needs high computational requirements due to the nonlinear nature of chemical reactions involved in the model formulation, which is also subject to uncertainties. In the context of ozone as an air pollutant, the determination of the background ozone level (BOL), independent from human activities, is also important as it could represent one of reliable references to human health risk assessment. The concept of BOL may be easily understood, but practically, it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method is presented in this work, by evaluating the relationship of ozone versus nitric oxide (O3-NO) to estimate the BOL value, mainly by using night-time and early morning measurement data collected at the monitoring stations. In this thesis, to deal with the challenging problem of air pollutant profile estimation, a metamodel approach is suggested to adequately approximate intrinsically nonlinear and complex input-output relationships with significantly less computation. The intrinsic characteristics of the underlying physics are not assumed to be known, while the system’s input and output behaviours remain essential. A considerable number of metamodels approach have been proposed in the literature, e.g. splines, neural networks, kriging and support vector machine. Here, the radial basis function neural network (RBFNN) is concerned as it is known to offer good estimation performance on accuracy, robustness, versatility, sample size, efficiency, and simplicity as compared to other stochastic approaches. The development requirements are that the proposed metamodels should be capable of estimating the ozone profiles and its background level temporally and spatially with reasonably good accuracies, subject to satisfying some statistical criteria. Academic contributions of this thesis include in a number of performance enhancements of the RBFNN algorithms. Generally, three difficulties involved in the network training, selection of radial basis centres, selection of the basis function variance (i.e. spread parameter), and training of network weights. The selection of those parameters is very crucial, as they directly affect the number of hidden neurons used and also the network overall performance. In this research, some improvements of the typical RBFNN algorithm (i.e. orthogonal least squares) are achieved. First, an adaptively-tuned spread parameter and a pruning algorithm to optimise the network’s size are proposed. Next, a new approach for training the RBFNN is presented, which involves the forward selection method for selecting the radial basis centres. Also, a method for training the network output weights is developed, including some suggestions for estimation of the best possible values of the network parameters by considering the cross-validation approach. For applications, results show that the combination of the proposed paradigm could offer a sub-optimal solution of metamodelling development in the generic sense (by avoiding the iteration process) for a faster computation, which is essential in air pollutant profile estimation

머신러닝 모델을 사용한 2002~2020년 한국의 O3, NO2, CO 농도의 고해상도 추정

학위논문(석사) -- 서울대학교대학원 : 보건대학원 보건학과, 2023. 2. 김호.Backrgound : Long-term exposure to ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO) is known to cause various diseases and increase mortality. For that reason, estimating ground-level O3, NO2, and CO concentrations with a high spatial resolution is crucial for assessing the health effects associated with these air pollutants. However, related studies are limited in South Korea. This study aimed to develop machine learning-based models to predict the monthly O3 (average of daily 8-hour maximums), NO2, and CO at a spatial resolution of 1 km × 1 km across South Korea from 2002 to 2020. Methods : Approximately 80% of the monitoring stations were used to train the three machine learning models (random forest, light gradient boosting, and neural network) with a 10-fold cross-validation, and 20% of the monitoring stations were used to test the model performance. The author also applied ensemble models to integrate the variation in predictions among the models. Multiple predictors with satellite-based remote sensing data, inverse distance weighted ground-level air pollutants, land use variables, reanalysis datasets for meteorological variables, and regional socioeconmoic variables collected from various databases were included in the prediction model. Results : For O3, the overall R2 of the ensemble model was 0.841 during the entire study period. Urban areas showed a better model performance (R2 = 0.845) than rural areas (R2 = 0.762). For NO2, the highest overall R2 was 0.756, which best fit in autumn (R2 = 0.768). For CO, the overall R2 value was 0.506. This study provides high spatial resolution monthly average O3 and NO2 estimates with excellent performance (R2 > 0.75). Conclusion : The authors predictions can be used to analyze the spatial patterns in pollutants in relation to population characteristics and studies on the health effects of long-term exposure to air pollution using geocode-based health information and local health data.연구배경 : 오존(O3), 이산화질소(NO2), 일산화탄소(CO)에 장기간 노출되면 각종 질병을 유발하고 사망률을 높이는 것으로 알려져 있다. 그렇기에, 고해상도로 지표면 O3, NO2, CO 농도를 추정하는 것은 이러한 대기오염물질과 관련된 건강 영향을 평가하는 데 매우 중요하다. 하지만, 장기간에 걸쳐 고해상도로 가스상 대기오염물질(O3, NO2, CO)를 추정한 연구는 국내에서 아직 진행된 바가 없다. 따라서, 본 연구는 2002년부터 2020년까지 대한민국 전역에서 1km × 1km의 공간해상도로 월별 O3(일평균 8시간 최대치), NO2, CO를 머신러닝 기반 모델 및 그들의 앙상블 모형을 통해 예측하고자 한다. 연구방법 : 3가지 머신러닝 모델(랜덤 포레스트, 라이트 그래디언트 부스팅, 신경망)의 최적의 파라미터를 찾기 위해 모니터링 스테이션의 약 80%를 훈련 데이터로 사용하였고, 10-fold 교차검증을 통해 훈련 데이터 내에서 훈련/평가 단계를 거쳤으며, 나머지 모니터링 스테이션의 20%를 모델 평가에 사용하였다. 여기에 추가로 머신러닝 모델 간의 예측 변동을 통합하기 위해 앙상블 모델을 적용했다. 데이터에는 위성 기반 원격 감지 데이터, 역거리 가중치 기반 대기오염농도, 토지 이용 변수, 기상 재분석 자료, 다양한 데이터베이스에서 수집된 지역 사회경제적 변수 등이 포함되었다. 연구결과 : O3의 경우, 전체 연구 기간 동안 앙상블 모델의 R2가 0.841을 기록했으며, 도시 지역이 농촌 지역(R2 = 0.762)보다 우수한 예측 성능(R2 = 0.845)을 보였다. NO2의 경우, 앙상블(평균) 모델의 R2가 0.756으로 가장 높았으며, 계절로 보면 가을에 예측 성능이 가장 높았다(R2 = 0.768). CO의 경우, R2가 0.506 을 기록했다. 본 연구는 O3 및 NO2 에서 R2 > 0.75 으로 높은 예측력의 고해상도 월평균 추정치를 제공한다. 결론 : 본 연구에서 얻어진 대기오염 추정 결과는 인구 특성과 관련된 가스상 대기오염물질의 공간 패턴을 분석하거나, 위치 기반 건강 정보와 행정구역 단위 건강 데이터와 엮여서 장기간 대기오염 노출의 건강 영향을 평가하는 연구에 사용될 수 있을 것으로 기대된다.Chapter 1. Introduction 1 Chapter 2. Materials and Methods 6 2.1. Study area 6 2.2. Air pollution monitoring data 6 2.3. Satellite-based remote sensing data 7 2.3.1. Meteorological data 7 2.3.2. Land-use data 10 2.3.3. Surface reflectance 11 2.4. Regional socioeconomic predictors 12 2.5. Modeling procedures 13 2.5.1. Data Preprocessing 14 2.5.2. Machine learning-based model 15 2.5.3. Ensemble Model 16 2.5.4. Model Prediction 17 Chapter 3. Results 19 Chapter 4. Discussion 29 Chapter 5. Conclusion 34 Supplementary materials 47 국문 초록 82 Tables Table 1. Model performance for O3, NO2, and CO overall and in three- and four-year periods 21 Table S1. Detailed information about data sources 61 Table S2. Variables sorted by % missing values 65 Table S3. Results of parameter grid search using 10-fold cross-validation for O3, NO2 and CO 68 Table S4. Yearly ensemble (GAM) performance for O3, NO2, and CO 70 Table S5. Model performances for O3, NO2, and CO by season and urbanity 71 Table S6. Number of monitoring stations by year for O3, NO2 and CO in urban and rural areas 73 Figures Fig. 1. Flowchart of the modeling process. GEE: Google Earth Engine, SEDAC: Socioeconomic Data and Applications Center, RSD: Regional Socioeconomic Database from Korean Disease Control and Prevention Agency 18 Fig. 2. Density scatter plot for monthly averages of the monitored and predicted concentrations of O3, NO2, and CO 26 Fig. 3. Maps of monitored and predicted O3, NO2 and CO during 2002~2020 27 Fig. 4. Percentage decrease in R2 when excluding grouped variables from each machine learning model of O3, NO2, and CO. The closer the color is to red, the greater the effect of the variables on the model performance 28 Fig. S1. Urban/Rural and Metropolitan (Metro) area for entire contiguous regions of South Korea 74 Fig. S2. Distribution maps of predicted O3 (ppb) by year and season for contiguous South Korea 75 Fig. S3. Distribution maps of predicted NO2 (ppb) by year and season for contiguous South Korea 76 Fig. S4. Distribution maps of predicted CO (ppm) by year and season for contiguous South Korea 77 Fig. S5. Monthly fluctuations in the number of monitoring stations for O3, NO2, and CO between 2002 and 2020 78 Fig. S6. Density scatter plot for monthly averages of the monitored and predicted concentrations of O3, NO2, and CO with seasonal discrimination 79석

A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden

Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO 2 ) and ozone (O 3 ) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005-2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM 10 (PM<10 microns), PM 2.5 (PM<2.5 microns), PM 2.5-10 (PM between 2.5 and 10 microns), NO 2 , and O 3 for each squared kilometer of Sweden over the period 2005-2016. Our models were able to describe between 64% (PM 10 ) and 78% (O 3 ) of air pollutant variability in held-out observations, and between 37% (NO 2 ) and 61% (O 3 ) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigation

Spatiotemporal and temporal forecasting of ambient air pollution levels through data-intensive hybrid artificial neural network models

Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites.Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites

Advances in air quality research – current and emerging challenges

This review provides a community\u27s perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy

Advances in air quality research – current and emerging challenges

© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0/This review provides a community’s perspective on air quality research focusing mainly on developmentsover the past decade. The article provides perspectives on current and future challenges as well asresearch needs for selected key topics. While this paper is not an exhaustive review of all research areas in thefield of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizingsources and emissions of air pollution, new air quality observations and instrumentation, advances in air qualityprediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure andhealth assessment, and air quality management and policy. In conducting the review, specific objectives were(i) to address current developments that push the boundaries of air quality research forward, (ii) to highlightthe emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guidethe direction for future research within the wider community. This review also identifies areas of particular importancefor air quality policy. The original concept of this review was borne at the International Conferenceon Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the articleincorporates a wider landscape of research literature within the field of air quality science. On air pollutionemissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources,particulate matter chemical components, shipping emissions, and the importance of considering both indoor andoutdoor sources. There is a growing need to have integrated air pollution and related observations from bothground-based and remote sensing instruments, including in particular those on satellites. The research shouldalso capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which areregulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue,with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time,one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerablepotential by providing a consistent framework for treating scales and processes, especially where thereare significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposureto air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of moresophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments.With particulate matter being one of the most important pollutants for health, research is indicating the urgentneed to understand, in particular, the role of particle number and chemical components in terms of health impact,which in turn requires improved emission inventories and models for predicting high-resolution distributions ofthese metrics over cities. The review also examines how air pollution management needs to adapt to the abovementionednew challenges and briefly considers the implications from the COVID-19 pandemic for air quality.Finally, we provide recommendations for air quality research and support for policy.Peer reviewe

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Abstract Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013–2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO2. 50% of the population was estimated to be exposed to NO2 concentrations between 12 and 38 μg/m3 and PM10 between 20 and 35 μg/m3. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions

Urban air pollution modelling with machine learning using fixed and mobile sensors

Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution. The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018). The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction