Air pollution exposure assessment in sparsely monitored settings; applying machine-learning methods with remote sensing data in South Africa.

Air pollution is one of the leading environmental risk factors to human health – Both short and long-term exposure to air pollution impact human health accounting for over 4 million deaths. Although the risk of exposure to air pollution has been quantified in different settings and countries of the world. The majority of these studies are from high-income countries with historical air pollutant measurement data and corresponding health outcomes data to conduct such epidemiological studies. Air pollution exposure levels in these high-income settings are lower than the exposure levels in low-income countries. The exposure level in sub-Saharan Africa (SSA) countries has continued to increase due to rapid industrialization and urbanization. In addition, the underlying susceptibility profile of SSA population is different from the profiles of the population in high-income settings. However, a major limitation to conducting epidemiological studies to quantify the exposure-response relationship between air pollution and adverse health outcomes in SSA is the paucity of historical air pollution measurement data to inform such epidemiological studies. South Africa an SSA country with some air quality monitoring stations especially in areas classified as air pollution priority areas have historical particulate matter less than or equal to 10 micrometres in aerodynamic diameter (PM10 μg/m3) measurement data. PM10 is one of the most monitored criteria for air pollutants in South Africa. The availability of satellite-derived aerosol optical depth (AOD) at high spatial and temporal resolutions provides information about how particles in the atmosphere can prevent sunlight from reaching the ground. This satellite product has been used as a proxy variable to explain ground-level air pollution levels in different settings. This thesis main objective was to use satellite-derived AOD to bridge the gap in ground-monitored PM10 across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape). We collected PM10 ground monitor measurement data from the South Africa Weather Services across the four provinces for the years 2010 – 2017. Due to the gaps in the daily PM10 across the sites and years. In study I, we compared methods for imputing daily ground-level PM10 data at sites across the four provinces for the years 2010 – 2017 using random forest (RF) models. The reliability of air pollution exposure models depends on how well the models capture the spatial and temporal variation of air pollution. Thus, study II explored the spatial and temporal variations in ground monitor PM10 across the four provinces for the years 2010 – 2017. To explore the feasibility of using satellite-derived AOD and other spatial and temporal predictor variables, Study III used an ensemble machine-learning framework of RF, extreme gradient boosting (XGBoost) and support vector regression (SVR) to calibrate daily ground-level PM10 at 1 × 1 km spatial resolution across the four provinces for the year 2016. In conclusion, we developed a spatiotemporal model to predict daily PM10 concentrations across four provinces of South Africa at 1 × 1 km spatial resolution for 2016. This model is the first attempt to use a satellite-derived product to fill the gap in ground monitor air pollution data in SSA

Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO$_x$)-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated $R^2$. Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS786 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Assessing the Value of Complex Refractive Index and Particle Density for Calibration of Low-Cost Particle Matter Sensor for Size-Resolved Particle Count and PM2.5 Measurements

Commercially available low-cost particulate matter (PM) sensors provide output as total or size-specific particle counts and mass concentrations. These quantities are not measured directly but are estimated by the original equipment manufacturers' (OEM) proprietary algorithms and have inherent limitations since particle scattering depends on their composition, size, shape, and complex index of refraction (CRI). Hence, there is a need to characterize and calibrate their performance under a controlled environment. We present calibration algorithms for Plantower PMS A003 sensor as a function of particle size and concentration. A standardized experimental protocol was used to control the PM level, environmental conditions and to evaluate sensor-to-sensor reproducibility. The calibration was based on tests when PMS A003 were exposed to different polydisperse standardized testing aerosols. The results suggested particle size distribution from PMS A003 was shifted compared to reference instrument measures. For calibration of number concentration, linear model without adjusting aerosol properties corrects the raw PMS A003 measurement for specific size bins with normalized mean absolute error within 4.0% of the reference instrument. Although the Bayesian Information Criterion suggests that models adjusting for particle optical properties and relative humidity are technically superior, they should be used with caution as the particle properties used in fitting were within a narrow range for challenge aerosols. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates and demonstrated lower error. These results have significant implications for using PMS A003 in high concentration environments, including indoor air quality and occupational/industrial exposure assessments, wildfire smoke, or near-source monitoring scenarios

The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks

Measurements at appropriate spatial and temporal scales are essential for understanding and monitoring spatially heterogeneous environments with complex and highly variable emission sources, such as in urban areas. However, the costs and complexity of conventional air quality measurement methods means that measurement networks are generally extremely sparse. In this paper we show that miniature, low-cost electrochemical gas sensors, traditionally used for sensing at parts-per-million (ppm) mixing ratios can, when suitably configured and operated, be used for parts-per-billion (ppb) level studies for gases relevant to urban air quality. Sensor nodes, in this case consisting of multiple individual electrochemical sensors, can be low-cost and highly portable, thus allowing the deployment of scalable high-density air quality sensor networks at fine spatial and temporal scales, and in both static and mobile configurations.This work was supported by EPSRC (grant number EP/E002102/1) and the Department for Transport

Intraurban Variability of Ambient Particulate Matter

An understanding of spatial and temporal variability in ambient particulate matter: PM) is important for effective air quality management and for assessing potential exposure misclassification in epidemiological and exposure studies used to support health-based standards. Spatiotemporal variability of PM in urban areas can be influenced by many factors, such as local sources of primary PM; source locations and their emission profiles; topographic barriers; meteorological patterns; behavior of semi-volatile components; and measurement errors. Intraurban variability is often gauged by conducting measurements at a network of monitoring stations across the region of interest. While certain statistical metrics are commonly used and interpreted in a relative sense across site-pairs, there is no standardized framework for analyzing such datasets. This dissertation presents systematic data analysis approaches applicable to a variety of monitoring networks for assessing intraurban variability in PM and its components. Interpreting patterns in statistical metrics for a network with a large number of sites can be particularly challenging, and calculating these metrics for each site with respect to a reference concentration time series may better reveal the variability. In the absence of a representative background site, the network itself can be utilized to generate baseline and site-specific excess concentration time series to semi-quantitatively differentiate urban- and larger-scale contributions from local-scale emissions. Utilizing this approach for interpretation of patterns in the statistical metrics provides insights into the factors influencing the baseline and the monitoring sites displaying greater variability. Apportionment of measured concentrations at each site into baseline and site-specific excess concentrations towards refined application of wind regression tools for estimating local emission source regions are also discussed. The approach is also utilized for identifying meteorological and geographic factors that modulate the spatial and temporal PM trends. It also provides a weight-of-evidence to conventional source apportionment tools used for estimating local and regional source impacts. The strengths and limitations of the proposed approaches are discussed for a variety of networks measuring PM and/or its components on varying spatial and temporal scales. Issues regarding measurement uncertainty estimation and precision in data reporting which can influence interpretation of variability are also discussed

Constraining chemical transport PM_2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources.Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275&thinsp;m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3−, 0.78 and 0.23 for SO42−, 1.00 and 1.01 for NH4+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30&thinsp;% and 13&thinsp;%, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO42− cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43&thinsp;% increase. Assessing this physical technique in a well-instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.</p

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

Spatial modelling of air pollution for open smart cities

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the world’s population are expected to further move into urban areas. This urban growth leads to various environmental, social and economic challenges in cities, hampering the Quality of Life (QoL). Although recent trends in technologies equip us with various tools and techniques that can help in improving quality of life, air pollution remains the ‘biggest environmental health risk’ for decades, impacting individuals’ quality of life and well-being according to World Health Organisation (WHO). Many efforts have been made to measure air quality, but the sparse arrangement of monitoring stations and the lack of data currently make it challenging to develop systems that can capture within-city air pollution variations. To solve this, flexible methods that allow air quality monitoring using easily accessible data sources at the city level are desirable. The present thesis seeks to widen the current knowledge concerning detailed air quality monitoring by developing approaches that can help in tackling existing gaps in the literature. The thesis presents five contributions which address the issues mentioned above. The first contribution is the choice of a statistical method which can help in utilising existing open data and overcoming challenges imposed by the bigness of data for detailed air pollution monitoring. The second contribution concerns the development of optimisation method which helps in identifying optimal locations for robust air pollution modelling in cities. The third contribution of the thesis is also an optimisation method which helps in initiating systematic volunteered geographic information (VGI) campaigns for detailed air pollution monitoring by addressing sparsity and scarcity challenges of air pollution data in cities. The fourth contribution is a study proposing the involvement of housing companies as a stakeholder in the participatory framework for air pollution data collection, which helps in overcoming certain gaps existing in VGI-based approaches. Finally, the fifth contribution is an open-hardware system that aids in collecting vehicular traffic data using WiFi signal strength. The developed hardware can help in overcoming traffic data scarcity in cities, which limits detailed air pollution monitoring. All the contributions are illustrated through case studies in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under the broader framework of the open smart city and for urban health research

Leveraging geospatial statistics for measuring and valuing the urban environment

This thesis looks at emerging uses of geospatial data for analysing the urban environment. As high-dimensional data becomes increasingly available, sophisticated spatial and temporal statistical estimation strategies can assess the minutia of environmental processes in a dynamic urban context. Each essay focuses on the improved measurement of high-resolution non-market environmental amenities and evaluating them using observed impacts on house prices or transportation networks. While valuation techniques for each amenity vary depending on context, these works all highlight a set of spatial methodologies for detailed urban analytics with a particular focus on urban greenery, seismic and flood risk, and pollution mitigation