Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Children’s Environmental Health and Disease Prevention Research

The National Children’s Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Children’s Environmental Health and Disease Prevention Research that may enhance the National Children’s Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatial–temporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) “Usual” temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Children’s Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period

Improving public health in smart cities in the air pollution context

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe public has continually developed interest in knowing the air quality around them. This is of great importance not only for planning their activities, but also for taking precautionary measures for their health. With support from smart cities infrastructure that supports taking measurements of pollutant concentrations, several countries and researchers have used the concept of air quality index (AQI) in its different forms of air quality or air pollution to interpret and communicate such measurements. In this study we have reviewed the implemented indices by government bodies and some formulations from researchers in relation to the available data to determine an optimum index for Madrid city. This comparison has helped to formulate the Madrid Local Air Quality Index (MLAQI), which considers the local situation in Madrid city. In relation to the available data from the city council, we have reviewed and compared some of the spatial interpolation methods that have been applied in the field of air pollution. This helped us to identify IDW for support of automated hourly pollution interpolation for the available data from Madrid pollution sensors. We have then used MLAQI and IDW to create an hourly pollution Web Feature service aimed at helping with public awareness of the air quality around them. The surfaces are categorised with the index categories from good to very poor categories with defined colour coding. We used the created service to develop a routing web application where high MLAQI categories of poor and very poor are used as polygon barriers to limit the route calculation in those polluted areas thereby helping the public to protect their health from such areas

Derivation of Black Carbon Proxies in an Integrated Urban Air Quality Monitoring Network

Air pollution is one of the biggest environmental health challenges in the world, especially in the urban regions where about 90% of the world’s population lives. Black carbon (BC) has been demonstrated to play an important role in climate change, air quality and potential risk for human beings. BC has also been suggested to associate better with health effects of aerosol particles than the commonly monitored particulate matter, which does not solely originate from combustion sources. Furthermore, BC has been recommended to be included as one of the parameters in air quality index (AQI) which is communicated to citizens. However, due to financial constraints and the lack of the national legislation, BC has yet been measured in every air quality monitoring station. Therefore, some researchers developed low-cost sensors which give indicative ambient BC concentrations as an alternative. Even so, due to instrument failure or data corruption, measurements by physical sensors are not always possible and long data gaps can exist. With missing data, the data analysis of interactions between air pollutants becomes more uncertain; therefore, air quality models are needed for data gap imputation and, moreover, for sensor virtualization. To complement the current deficiency, this thesis aims to derive statistical proxies as virtual sensors to estimate BC by using the current air quality monitoring network in Helsinki metropolitan area (HMA). To achieve this, we first characterized the ambient BC concentrations in four types of environments in HMA: traffic site (TR: 0.77–2.08 μg m−3), urban background (UB: 0.51–0.53 μg m−3), detached housing (DH: 0.64–0.80 μg m−3) and regional background (RB: 0.27–0.28 μg m−3). TR, in general, had higher BC concentrations due to the close proximity to vehicular emission but decreasing trends (–10.4 % yr−1) likely thanks to the fast renewal of the city bus fleet in HMA. UB, on the other hand, had a more diverse source of BC, including biomass burning and traffic combustion. Its trend had also been decreasing, but at a smaller rate (e.g. UB1: –5.9 % yr−1). We then narrowed down the dataset to a street canyon site and an urban background site for BC proxy derivation. At both sites, despite the low correlation with meteorological factors, BC correlated well with other commonly monitored air pollutant parameters by both reference instruments and low-cost sensors, such as NOx and PM2.5. Based on this close association, we developed a statistical proxy with adaptive selection of input variables, named input-adaptive proxy (IAP). This white-box model worked better in terms of accuracy at the street canyon site (R2 = 0.81–0.87) than the urban background site (R2 = 0.44–0.60) because of the scarce missing gaps in data in the street canyon. When compared with other white- and black-box models, IAP is preferred because of its flexibility and architectural transparency. We further demonstrated the feasibility of sensor virtualization by using statistical proxies like IAP at both sites. We also stressed that such virtual sensors are location specific, but it might be possible to extend the models from one street canyon site to another with a calibration factor. Similarly, the proposed methodology can also be applied to estimate other air pollutant parameters with scarcity of data, such as lung deposited surface area and ultrafine particles, to complement the existing AQI.

Air Quality Modelling for Ireland

Air pollution is the primary environmental cause of premature death in the EU (European Commission, 2013) and the most problematic pollutants across Europe have consistently been oxides of nitrogen (e.g. nitrogen dioxide (NO2)), particulate matter (e.g. PM10, PM2.5) and ozone (O3). While measurements form an important aspect of air quality assessment, on their own they are unlikely to be sufficient to provide an accurate spatial and temporal description of the pollutant concentrations for exposure assessment and moreover they cannot provide information regarding future air quality. Annex XVI of 2008/50/EC requires member states to “ensure that up to date information on ambient concentrations of the pollutants covered” by the Directive are “made available to the public”. This information must include actual or predicted exceedances of alert and information thresholds and a forecast for the following day of which a model is an integral part. As a result, air quality models are increasingly required for public information, air quality management and research purposes. The primary objectives of this research fellowship were to develop a calibrated air quality forecast model for Ireland capable of predicting the Air Quality Index for Health (AQIH) in each of the air quality zones in Ireland and to model the spatial variation in concentrations on a national scale. This research project has produced three different models for NO2, PM10, PM2.5, O3 and SO2, all of which are available for further use. These are: A hybrid point wise 48 hour forecast model; Spatial model (WS-LUR) to produce annual mean maps of air pollution on a national scale; Temporal WS-LUR model. A comprehensive review of modelling systems carried out at the outset of this research fellowship, together with consideration for key EPA objectives, informed the direction of model development. This review is available as a separate EPA report. A priority within the EPA was to produce air quality forecasts based on the AQIH. The AQIH is based on point wise measurements and in order to extrapolate these measurements to the future, statistical modelling was deemed the most suitable. The advantages of this approach were that it could be developed from first principles specific to the area of interest and completely (avoiding any reliance on a third party to supply the model or apply licensing restrictions) and the associated speed of forecast computation. Forecasts are only useful if they can be computed and made available to the public relatively quickly. The accuracy of such methods also tends to be high and of low bias as they are developed site-specifically unlike large scale deterministic models that are often developed and tested in vastly differing domains. In particular, this method was capable of producing accurate point wise forecasts without the need for a detailed emissions inventory. At the project outset, the emissions inventory was not of sufficient spatial resolution to make realistic point wise forecasts in all air quality zones by deterministic means and it would have been an inefficient use of resources to base the development of forecasts on what was currently available. Initial model development proceeded using time series analysis in conjunction with non-parametric kernel regression, with local meteorological parameters as predictor variables. A model validation study found that this technique produced accurate forecast of ozone and SO2 but had a tendency to under predict peak NO2 and PM10/2.5 concentrations. An analysis of air mass history using the HYSPLIT model was carried out which revealed certain air masses (primarily easterly and re-circulated air) were responsible for most incidence of elevated concentrations. The results of this study were used to develop a HYSPLIT add-on for the forecast model which operates by forecasting air mass history in real time and invoking a different forecasting methodology depending on the region of origin of the air. The ability of the hybrid point wise model to predict daily maximum hourly NO2, SO2, 8 hourly ozone and daily average PM10 and PM2.5 was demonstrated by comparing a full year of modelled data with measured data at each of the AQIH sites. Index of agreement values ranged from a low of 0.80 for SO2 to 0.88 for NO2 and ozone, while correlation coefficients ranged from a low of 0.69 for SO2 to 0.82 for NO2. Full results of this validation study are contained in a separate report. In order to provide detail on the spatial variation of concentration levels across the country, land use regression (LUR) was recommended in the model review as the most suitable technique. This technique uses surrogate spatial indicators to explain the variation in concentration levels between monitoring points. Land cover data (CORINE), DTM output, road density information and population data are all factors that influence concentration levels and data that were broadly available. In contrast to most LUR studies, circular buffers were not used in the determination of spatial predictor variables. Rather, a novel sector based approach (WS-LUR) was adopted whereby variables were calculated within 8 pre-defined sectors representing the major wind directions around each monitoring site. This approach had a dual purpose. Firstly, it accounts for the direction of influence of emission sources on air quality in a given location. Traditional LUR assumes equal influence of emissions in the area surrounding a monitoring site regardless of wind direction. This approximation may be reasonable in highly urbanised areas where emissions sources are relatively uniform in the surrounding region. However, in this study the regression was applied on a national scale and prevailing winds coupled with clear directional influenced at air quality monitoring sites meant that WS-LUR is a superior option. The second advantage of this methodology is that it increases the effective number of data points available for the regression analysis, resulting in a more robust final equation. In conjunction with research project (2013-EH-FS-7), a set of annual mean maps within a geographic information system (GIS) environment were created and validated for each of NO2, PM10, PM2.5, O3 and SO2. These provide a highly relevant source of information regarding spatial variation in concentration levels on a national scale which can be used not only for exposure studies and general air quality assessment, but also as a tool to correlate emission sources and surrogates with air quality. A temporal WS-LUR model was developed for NO2, Ozone and PM10 by including hourly meteorological data in conjunction with pre-specified spatial data as predictor variables. This model has the potential to provide fast, efficient national air quality forecast maps for Ireland with minimal computational requirements. This project has achieved key EPA objectives and has produced a fully automated and operational air quality model which produced twice-daily forecasts of the AQIH in each air quality zone in Ireland. The stepwise approach chosen for model development allowed deliverables prior to completion of the project while minimising associated risks. The models developed as part of this fellowship form solid building blocks on which future air quality modelling studies in Ireland can be based

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network.

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach

Spatio-temporal models for air pollution

Air pollution is the biggest environmental risk to global health and it is estimated that, globally, 7 million deaths can be attributed to air pollution each year \citep{WHO2018}. The World Bank estimates that, in 2016, the overall cost of ambient air pollution to the global economy was an estimated US \5.7 trillion or 4.4 per cent of global GDP \citep{worldbank}. A number of different air pollutants have been associated with adverse health effects, including fine particulate matter (PM_{2.5}$), nitrogen dioxide and ozone. In studies of the effects of air pollution, exposure information is often obtained from a fixed number of monitoring sites within the region of interest. However, an increasing number of models of air pollution are being used that provide estimates of concentrations. These are used to represent exposures at every location in a health study area, rather than just at a number of fixed measurement locations. Another use of modelling of air pollution is to provide short-term forecasts that can be used to inform the behaviour of vulnerable people. In this thesis, we develop statistical approaches to modelling, and forecasting, daily concentrations of$\mbox{PM}_{2.5}$in urban areas. We consider two different approaches, both in terms of model formulation and performing inference. The first approach is Dynamic Space-Time Models (DSTM). Under this framework, a \textit{data} model relates observations (measurements) to a \textit{process} model that specifies the dynamic evolution of the "true" underlying process. This approach is implemented using two different methods for estimation: methods of moments and expectation-maximisation. We also develop an approach using Bayesian Hierarchical Spatio-Temporal modelling (BHSTM). The inference is done using computational efficient methods for Bayesian inference (integrated nested Laplace approximations). This model allows predictions of daily PM$_{2.5}$over both space and time, which can be used to interpolate both past measurements and future predictions. Both approaches were implemented using data from Greater London, with their performance evaluated in terms of their ability to predict daily concentrations of PM$_{2.5}$over time at different measuring sites. Both methods were able to accurately predict future values of daily PM$_{2.5}$ at different locations, with one-day ahead predictions being more accurate than those used for longer periods, as might be expected. One of the major advantages of the BHSTM approach is that it provides a straightforward method for producing estimates of the uncertainty that is associated with predictions

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

Spatiotemporal modeling of air pollutants and their health effects in the Pittsburgh region

Air pollutants have been associated with adverse health outcomes such as cardiovascular and respiratory diseases through epidemiological studies. Spatiotemporal and spatial statistics are widely used in both exposure assessment and health risk estimation of air pollutants. In the current paper, spatiotemporal and spatial models are developed for and applied to four specfic topics about air pollutants: (1) estimating spatiotemporal variations of particulate matter with diameter less than 2.5 um (PM2.5) using monitoring data and satellite aerosol optical depth (AOD) measurements, (2) estimating long-term spatial variations of ozone (O3) using monitoring data and satellite O3 profile measurements, (3) spatiotemporal associating acute exposure of air pollutants to mortality, and (4) spatiotemporal associating chronic air pollution exposure to lung cancer incidence. Environmental, socioeconomic and health data from Allegheny county and the State of Pennsylvania are collected to illustrate these techniques. The public health significance of these studies includes characterizing the exposure level of air pollutants and their health risks for mortality caused by cardiovascular and respiratory diseases and lung cancer incidence in the Pittsburgh region and developing novel spatiotemporal models such as spatiotemporal generalized estimating equations for the regression analysis of spatiotemporal counts data, especially for the massive spatiotemporal data used in epidemiological studies