The relationship between short-term exposure to air pollution and mortality or morbidity has been the subject of much recent research, in which the standard method of analysis uses Poisson linear or additive models. In this paper we use a Bayesian dynamic generalised linear model (DGLM) to estimate this relationship, which allows the standard linear or additive model to be extended in two ways: (i) the long-term trend and temporal correlation present in the health data can be modelled by an autoregressive process rather than a smooth function of calendar time; (ii) the effects of air pollution are allowed to evolve over time. The efficacy of these two extensions are investigated by applying a series of dynamic and non-dynamic models to air pollution and mortality data from Greater London. A Bayesian approach is taken throughout, and a Markov chain monte carlo simulation algorithm is presented for inference. An alternative likelihood based analysis is also presented, in order to allow a direct comparison with the only previous analysis of air pollution and health data using a DGLM.
Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested $α$-stable random factors. The hierarchical structure facilitates Bayesian inference and offers a convenient and inter…
Estimation of the long-term health effects of air pollution is a challenging task, especially when modelling small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial correlation structure in these data, which is accounted for using random effects modelled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localised conditional autoregressive model is developed for the random effects. This …
The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, due to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the fun…
Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by environmental conditions, and usually need to be field-calibrated by collocating low-cost sensors with reference-grade instruments. We show, theoretically and empirically, that the common procedure of regression-based calibration using collocated data systematically underestimates high air pollution concentrations, which are critical to diagnose from a heal…
Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects is fine particulate matter ambient pollution (PM$_{2.5}$) to which an estimated 3 million deaths can be attributed annually. The primary source of information for estimating exposures has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is limited. Ground monitoring data therefore needs to be s…
In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions. We aim at demonstrating how the combination of multiple data sources, such as dispersion model outputs, ground observations and covariates, leads to more accurate predictions of air pollution at grid level. We consider nitrogen dioxide (NO2) concentration in Great…
Air pollution is a serious issue that currently affects many industrial cities in the world and can cause severe illness to the population. In particular, it has been proven that extreme high levels of airborne contaminants have dangerous short-term effects on human health, in terms of increased hospital admissions for cardiovascular and respiratory diseases and increased mortality risk. For these reasons, accurate estimation and prediction of airborne pollutant concentration is crucial. In this paper, we propose a flexible novel approach to model hourly measurements of fine particulate matter…
University of Sheffield（谢菲尔德大学）；Makerere University（麦克雷雷大学）
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially an issue if methods from resource-rich regions are applied without handling these additional constraints. In this paper we show, through the use of an air pollution network example, how using probabilistic machine learning can mitigate some of the technical constraints. Specifically we experiment with modelling the calibration for individu…
A Bayesian multiple change-point model is proposed to analyse violations of air quality standards by pollutants such as nitrogen oxides (NO2 and NO) and carbon monoxide (CO). The model is built on the assumption that the occurrence of threshold exceedances may be described by a non-homogeneous Poisson process with a step rate function. Unlike earlier approaches, our model is not restricted by a predetermined number of change-points, nor does it involve any covariates. Possible short-range correlations in the exceedance data (e.g., due to chemical and meteorological factors) are removed via dec…
Indian Institute of Technology Gandhinagar（印度理工学院甘地纳加尔分校）
Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm,…
Air pollution constitutes the highest environmental risk factor in relation to heath. In order to provide the evidence required for health impact analyses, to inform policy and to develop potential mitigation strategies comprehensive information is required on the state of air pollution. Information on air pollution traditionally comes from ground monitoring (GM) networks but these may not be able to provide sufficient coverage and may need to be supplemented with information from other sources (e.g. chemical transport models; CTMs). However, these may only be available on grids and may not ca…
Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations and air quality and health at any given location are likely impacted by interventions at many sources. The structure of interference in this context is dictated by complex atmospheric processes governing how pollution emitted from a particular source is transformed and transported across space, and can be cast with a bipartite structure reflecting the two distinct types of units: 1) interventional un…
Variance-based variable selection in sensor calibration with strong interferents -- application to air pollution monitoring with a carbon nanotube sensor array
This paper describes an agent based approach for simulating the control of an air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD) is combined with an Artificial Neural Network (ANN) to predict the concentration levels of three different air pollutants. The two models (GPM and ANN) are integrated with a MAS (multi-agent system). The MAS models pollutant sources controllers and air pollution monitoring agencies as software agents. The population of agents cooperates with each other in order to reduce their emissions and control the air pollution. Leaks or natural sources o…
The opportunity to assess short term impact of air pollution relies on the causal interpretation of the exposure-outcome association, but up to now few studies explicitly faced this issue within a causal inference framework. In this paper, we reformulated the problem of assessing the short term impact of air pollution on health using the potential outcome approach to causal inference. We focused on the impact of high daily levels of PM10 on mortality within two days from the exposure in the metropolitan area of Milan (Italy), during the period 2003-2006. After defining the number of attributab…
Development of air pollution models for large regions is a priority for population-based epidemiologic studies. The rapid development of big data information systems and machine learning algorithms have opened new grounds for refinements of current model frameworks. This commentary overviews recent contributions and outlines extensions from geostatistics and machine learning perspectives. For the coming years, expected advances will expand the use of learning algorithms to model spatial trends and incorporate spatial covariance models in the learning processes. These extensions will refine exi…
Studies have shown that exposure to air pollution, even at low levels, significantly increases mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to reduce air pollution exposure is needed. In this paper, we introduce a novel statistical method that splits the data into two subsamples: (a) Using the first subsample, we consider a data-driven search for $\textit{de novo}$ discovery of subgroups that could have exposure effects that differ from the population mean; and then (b) using the second subsample, we q…
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 s…
University of Delaware（特拉华大学）；PingAn Technology（平安科技）；South China University of Technology（华南理工大学）
Air pollution poses a serious threat to human health as well as economic development around the world. To meet the increasing demand for accurate predictions for air pollutions, we proposed a Deep Inferential Spatial-Temporal Network to deal with the complicated non-linear spatial and temporal correlations. We forecast three air pollutants (i.e., PM2.5, PM10 and O3) of monitoring stations over the next 48 hours, using a hybrid deep learning model consists of inferential predictor (inference for regions without air pollution readings), spatial predictor (capturing spatial correlations using CNN…
Air pollution is a pernicious but increasingly rampant health problem in human populations across the globe. Varying duration of exposure to airborne pollutants can result in a plethora of health conditions, ranging from short-term bronchitis to prolonged cardiovascular diseases and lung cancers. Under chronic exposure to pollution, medical treatment for pollutant-caused health conditions can be quite costly. The most immediate and effective way to combat the health detriments of pollution is air purifiers, which take in ambient air and filter out the harmful pollutants before releasing it bac…
Statistical techniques used in air pollution modelling usually lack the possibility to understand which predictors affect air pollution in which functional form; and are not able to regress on exceedances over certain thresholds imposed by authorities directly. The latter naturally induce conditional quantiles and reflect the seriousness of particular events. In the present paper we focus on this important aspect by developing quantile regression models further. We propose a general Bayesian effect selection approach for additive quantile regression within a highly interpretable framework. We …
Air pollution is known to be a major threat for human and ecosystem health. A proper understanding of the factors generating pollution and of the behavior of air pollution in time is crucial to support the development of effective policies aiming at the reduction of pollutant concentration. This paper considers the hourly time series of three pollutants, namely NO$_2$, O$_3$ and PM$_{2.5}$, collected on sixteen measurement stations in Switzerland. The air pollution patterns due to the location of measurement stations and their relationship with anthropogenic activities, and specifically land u…
Vehicular air pollution has created an ongoing air quality and public health crisis. Despite growing knowledge of racial injustice in exposure levels, less is known about the relationship between the production of and exposure to such pollution. This study assesses pollution burden by testing whether local populations' vehicular air pollution exposure is proportional to how much they drive. Through a Los Angeles, California case study we examine how this relates to race, ethnicity, and socioeconomic status -- and how these relationships vary across the region. We find that, all else equal, tra…
We propose a new approach to estimate the causal effects of maritime traffic when natural or policy experiments are not available. We apply this method to the case of Marseille, a large Mediterranean port city, where air pollution emitted by cruise vessels is a growing concern. Using a recent matching algorithm designed for time series data, we create hypothetical randomized experiments to estimate the change in local air pollution caused by a short-term increase in cruise traffic. We then rely on randomization inference to compute nonparametric 95\% uncertainty intervals. We find that cruise …
Pollution has become a major threat in almost all metropolitan cities around the world. Currently, atmospheric scientists are working on various models that could help us understand air pollution. In this paper, we have formulated a new metric tool called Delhi Similarity Index (DSI). The DSI is defined as the geometrical mean of the trace gases such as ozone, sulfur-dioxide and carbon-monoxide, which ranges from 0 (dissimilar to Delhi) to 0.9-1 (similar to Delhi). The limitation of the tool concerning the result of the nitrous-di-oxide data set is also analyzed. Also, the GIS projections of P…
Motivated by analyzing a national data base of annual air pollution and cardiovascular disease mortality rate for 3100 counties in the U.S. (areal data), we develop a novel statistical framework to automatically detect spatially varying region-wise associations between air pollution exposures and health outcomes. The automatic region-wise spatially varying coefficient model consists three parts: we first compute the similarity matrix between the exposure-health outcome associations of all spatial units, then segment the whole map into a set of disjoint regions based on the adjacency matrix wit…
A wealth of epidemiological data suggests an association between mortality/morbidity from pulmonary and cardiovascular adverse events and air pollution, but uncertainty remains as to the extent implied by those associations although the abundance of the data. In this paper we describe an SSA (Singular Spectrum Analysis) based approach in order to decompose the time-series of particulate matter concentration into a set of exposure variables, each one representing a different timescale. We implement our methodology to investigate both acute and long-term effects of $PM_{10}$ exposure on morbidit…
The first part of this paper introduces a portfolio approach for quantifying the risk measures of pollution risk in the presence of dependence of PM$_{2.5}$ concentration of cities. The model is based on a copula dependence structure. For assessing model parameters, we analyze a limited data set of PM$_{2.5}$ levels of Beijing, Tianjin, Chengde, Hengshui, and Xingtai. This process reveals a better fit for the t-copula dependence structure with generalized hyperbolic marginal distributions for the PM$_{2.5}$ log-ratios of the cities. Furthermore, we show how to efficiently simulate risk measure…
While human mobility plays a crucial role in determining air pollution exposures and health risks, research to-date has assessed risks based solely on residential location. Here we leveraged a database of ~ 130 million workers in the US and published PM2.5 data between 2011-2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures (W) relative to home exposures (H), as well as increasing exposures for non-white and less educated workers relative …
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-res…
With their continued increase in coverage and quality, data collected from personal air quality monitors has become an increasingly valuable tool to complement existing public health monitoring systems over urban areas. However, the potential of using such `citizen science data' for automatic early warning systems is hampered by the lack of models able to capture the high resolution, nonlinear spatio-temporal features stemming from local emission sources such as traffic, residential heating and commercial activities. In this work, we propose a machine learning approach to forecast high frequen…
Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units. In particular, NLC is a challenge for evaluating the effects of environmental policies and climate events on health-related outcomes such as air pollution exposure. This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interferen…
In economics we often face a system, which intrinsically imposes a structure of hierarchy of its components, i.e., in modelling trade accounts related to foreign exchange or in optimization of regional air protection policy. A problem of reconciliation of forecasts obtained on different levels of hierarchy has been addressed in the statistical and econometric literature for many times and concerns bringing together forecasts obtained independently at different levels of hierarchy. This paper deals with this issue in case of a hierarchical functional time series. We present and critically discu…
Ambient fine particulate matter less than 2.5 $μ$m in aerodynamic diameter (PM$_{2.5}$) has been linked to various adverse health outcomes and has, therefore, gained interest in public health. However, the sparsity of air quality monitors greatly restricts the spatio-temporal coverage of PM$_{2.5}$ measurements, limiting the accuracy of PM$_{2.5}$-related health studies. We develop a method to combine estimates for PM$_{2.5}$ using satellite-retrieved aerosol optical depth (AOD) and simulations from the Community Multiscale Air Quality (CMAQ) modeling system. While most previous methods utiliz…
Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM$_{2.5}$), in which data is usually not measured at all study locations. PM$_{2.5}$ is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lower-dimensional representative scores of such multi-pollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatia…
Walking and cycling are promoted to encourage sustainable travel behavior among children and adults. School children during their travel episode to-and-from school are disproportionately exposed to air pollution due to multiple reasons such as proximity to high traffic roads and peak volumes. This paper presents a route to school informational intervention that was developed incorporating approaches and methods suggested in the literature for effective behavioral interventions. The intervention was implemented using escorting parents/guardians (N=104) of school children of Antwerp, Belgium to …
Predicting the occurrence, level and duration of high air pollution concentrations exceeding a given critical level enables researchers to study the health impact of road traffic on local air quality and to inform public policy action. Precise estimates of the probabilities of occurrence and level of extreme concentrations are formidable due to the combination of complex physical and chemical processes involved. This underpins the need for developing sophisticated extreme value models, in particular allowing for non-stationarity of environmental time series. In this paper, extremes of nitrogen…
Centre for Speckled Computing（散斑计算中心）；University of Edinburgh（爱丁堡大学）
According to the Lancet report on the global burden of disease published in October 2020, air pollution is among the five highest risk factors for global health, reducing life expectancy on average by 20 months. This paper describes a data-driven method for establishing causal relationships within and between two multivariate time series data streams derived from wearable sensors: personal exposure to airborne particulate matter of aerodynamic sizes less than 2.5um (PM2.5) gathered from the Airspeck monitor worn on the person and continuous respiratory rate (breaths per minute) measured by the…
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
