scholarly journals Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies

2020 ◽  
Vol 17 (14) ◽  
pp. 4914 ◽  
Author(s):  
Ronan Hart ◽  
Lu Liang ◽  
Pinliang Dong
Keyword(s):  
Air Pollution ◽  
Panel Data Analysis ◽  
Fine Particulate Matter ◽  
Pollution Level ◽  
University Campus ◽  
Significance Level ◽  
Vegetation Height ◽  
Circular Buffer ◽  
Spatio Temporal ◽  
Analysis Models

Fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is highly variable in space and time. In this study, the dynamics of PM2.5 concentrations were mapped at high spatio-temporal resolutions using bicycle-based, mobile measures on a university campus. Significant diurnal and daily variations were revealed over the two-week survey, with the PM2.5 concentration peaking during the evening rush hours. A range of predictor variables that have been proven useful in estimating the pollution level was derived from Geographic Information System, high-resolution airborne images, and Light Detection and Ranging (LiDAR) datasets. Considering the complex interplay among landscape, wind, and air pollution, variables influencing the PM2.5 dynamics were quantified under a new wind wedge-based system that incorporates wind effects. Panel data analysis models identified eight natural and built environment variables as the most significant determinants of local-scale air quality (including four meteorological factors, distance to major roads, vegetation footprint, and building and vegetation height). The higher significance level of variables calculated using the wind wedge system as compared to the conventional circular buffer highlights the importance of incorporating the relative position of emission sources and receptors in modeling.

scholarly journals Analysis of Spatio-Temporal Heterogeneity and Socioeconomic Driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1324
Author(s):  
Ju Wang ◽  
Ran Li ◽  
Kexin Xue ◽  
Chunsheng Fang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Shandong Province ◽  
Temporal Heterogeneity ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Spatio Temporal ◽  
Surrounding Areas ◽  
Pm2.5 Concentration

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

scholarly journals Space–Time Relationship between Short-Term Exposure to Fine and Coarse Particles and Mortality in a Nationwide Analysis of Korea: A Bayesian Hierarchical Spatio-Temporal Model

2019 ◽  
Vol 16 (12) ◽  
pp. 2111 ◽  
Author(s):  
Dayun Kang ◽  
Yujin Jang ◽  
Hyunho Choi ◽  
Seung-sik Hwang ◽  
Younseo Koo ◽  
...  
Keyword(s):  
Air Pollution ◽  
South Korea ◽  
Fine Particulate Matter ◽  
Model Fitting ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Respiratory Mortality ◽  
Short Term Exposure ◽  
Temporal Model ◽  
Spatio Temporal

Previous studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research gap by examining the association between air pollution and cause-specific mortality in South Korea between 2012 and 2015 using a two-stage Bayesian spatio-temporal model. We used 2012–2014 mortality and air pollution data for parameter estimation (i.e., model fitting) and 2015 data for model validation. Our results suggest that the relative risks of total, cardiovascular, and respiratory mortality were 1.028, 1.047, and 1.045, respectively, with every 10-µg/m3 increase in monthly PM2.5 (fine particulate matter) exposure. These findings warrant protection of populations who experience elevated ambient air pollution exposure to mitigate mortality burden in South Korea.

scholarly journals A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain

2020 ◽  
Vol 12 (22) ◽  
pp. 3803
Author(s):  
Rochelle Schneider ◽  
Ana M. Vicedo-Cabrera ◽  
Francesco Sera ◽  
Pierre Masselot ◽  
Massimo Stafoggia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Great Britain ◽  
Fine Particulate Matter ◽  
Epidemiological Studies ◽  
Learning Model ◽  
Additional Information ◽  
Multi Stage ◽  
Machine Learning Model ◽  
Spatio Temporal

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

Spatial modeling of air pollution based on data streams from geosensor networks

2018 ◽  
Vol 930 (12) ◽  
pp. 39-43 ◽  
Author(s):  
V.P. Savinikh ◽  
A.A. Maiorov ◽  
A.V. Materuhin
Keyword(s):  
Air Pollution ◽  
Data Streams ◽  
Spatial Modeling ◽  
Spatial Model ◽  
Large City ◽  
Field Tests ◽  
Processing Unit ◽  
Geosensor Network ◽  
Spatio Temporal ◽  
Different Sources

The article is a brief summary of current research results of the authors in the field of spatial modeling of air pollution based on spatio-temporal data streams from geosensor networks. The urban environment is characterized by the presence of a large number of different sources of emissions and rapidly proceeding processes of contamination spread. So for the development of an adequate spatial model is required to make measurements with a large spatial and temporal resolution. It is shown that geosensor network provide researchers with the opportunity to obtain data with the necessary spatio-temporal detail. The article describes a prototype of a geosensor network to build a detailed spatial model of air pollution in a large city. To create a geosensor in the prototype of the system, calibrated gas sensors for a nitrogen dioxide and carbon monoxide concentrations measurement were interfaced to the module, which consist of processing unit and communication unit. At present, the authors of the article conduct field tests of the prototype developed.

scholarly journals A global association between Covid-19 cases and airborne particulate matter at regional level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Angelo Solimini ◽  
F. Filipponi ◽  
D. Alunni Fegatelli ◽  
B. Caputo ◽  
C. M. De Marco ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Negative Binomial ◽  
Numerical Models ◽  
Regional Level ◽  
Pollution Level ◽  
Observation Data ◽  
Cumulative Number ◽  
Mixed Effect

AbstractEvidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM10, PM2.5), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m3 increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM2.5 and PM10 respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.

scholarly journals Air Pollution, Neonatal Immune Responses, and Potential Joint Effects of Maternal Depression

2021 ◽  
Vol 18 (10) ◽  
pp. 5062
Author(s):  
Jill Hahn ◽  
Diane R. Gold ◽  
Brent A. Coull ◽  
Marie C. McCormick ◽  
Patricia W. Finn ◽  
...  
Keyword(s):  
Air Pollution ◽  
Immune Responses ◽  
Cord Blood ◽  
Maternal Depression ◽  
Mononuclear Cells ◽  
Fine Particulate Matter ◽  
Depression Scale ◽  
Traffic Density ◽  
Tobit Regression ◽  
Adverse Health Effects

Prenatal maternal exposure to air pollution may cause adverse health effects in offspring, potentially through altered immune responses. Maternal psychosocial distress can also alter immune function and may increase gestational vulnerability to air pollution exposure. We investigated whether prenatal exposure to air pollution is associated with altered immune responses in cord blood mononuclear cells (CBMCs) and potential modification by maternal depression in 463 women recruited in early pregnancy (1999–2001) into the Project Viva longitudinal cohort. We estimated black carbon (BC), fine particulate matter (PM2.5), residential proximity to major roadways, and near-residence traffic density, averaged over pregnancy. Women reported depressive symptoms in mid-pregnancy (Edinburgh Postnatal Depression Scale) and depression history by questionnaire. Immune responses were assayed by concentrations of three cytokines (IL-6, IL-10, and TNF-α), in unstimulated or stimulated (phytohemagglutinin (PHA), cockroach extract (Bla g 2), house dust mite extract (Der f 1)) CBMCs. Using multivariable linear or Tobit regression analyses, we found that CBMCs production of IL-6, TNF-a, and IL-10 were all lower in mothers exposed to higher levels of PM2.5 during pregnancy. A suggestive but not statistically significant pattern of lower cord blood cytokine concentrations from ever (versus never) depressed women exposed to PM2.5, BC, or traffic was also observed and warrants further study.

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