scholarly journals Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut Using the Cellular Device Network

2019 ◽  
Vol 25 (1) ◽  
pp. 54-73
Author(s):  
Owais Gilani ◽  
Simon Urbanek ◽  
Michael J. Kane
Keyword(s):  
Urban Areas ◽  
Spatial Models ◽  
Epidemiologic Studies ◽  
Air Pollutant ◽  
Ozone Exposure ◽  
Ozone Pollution ◽  
Adverse Health Outcomes ◽  
Mobility Data ◽  
Pollutant Concentrations ◽  
The Difference

Abstract Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school, or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut, USA during a week in July 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-h maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone. Supplementary materials accompanying this paper appear online.

Meteorology-normalized impact of COVID-19 lockdown upon NO2 and O3 in Spain 

2021 ◽  
Author(s):  
Hervé Petetin ◽  
Dene Bowdalo ◽  
Hicham Achebak ◽  
Albert Soret ◽  
Marc Guevara ◽  
...  
Keyword(s):  
Urban Areas ◽  
Statistical Significance ◽  
Chem Phys ◽  
Reanalysis Data ◽  
Air Pollutant ◽  
Urban Background ◽  
Pollutant Concentrations ◽  
Trade Offs ◽  
The Difference ◽  
The Impact

<p>The mobility restrictions implemented to slow down the transmission of the new coronavirus disease (COVID-19) drastically altered Spanish anthropogenic emissions in several sectors, leading to substantial impacts on air pollutant concentrations. In order to reliably quantify these changes, the confounding effects of meteorological variability need to be properly taken into account. We thus designed an innovative methodology relying on the use of machine learning (ML) models fed with ERA5 meteorological reanalysis data and other time features, to estimate more accurately the so-called business-as-usual (BAU) pollutant concentrations that would have been observed in the absence of lockdown (Petetin et al., 2020). The difference with concentrations actually observed during the lockdown give meteorology-normalized estimates of the AQ changes due to the altered anthropogenic emission forcing, independently from the meteorological variability. Importantly, our methodology includes a conservative estimation of the uncertainties, which allows to highlight statistically significant changes. This study focuses on NO2 and O3. We applied this analysis for a selection of urban background and traffic stations covering more than 50 Spanish provinces and islands. Validation results indicate that the method usually performs well for estimating BAU concentrations (mean absolute bias below +6%, root mean square error around 25-30% and correlation above 0.80).</p><p>The COVID-19-related lockdown has induced a strong reduction (-50% on average) of NO2 concentrations in Spanish urban areas, although with some spatial variability among the provinces. In largest cities, stronger reductions were found at traffic stations compared to urban background ones, reflecting the major impact of the lockdown on traffic emissions. Substantial discrepancies with changes obtained considering a climatological averaged NO2 concentrations were found, highlighting the interest of such ML-based weather-normalization method. Compared to NO2, the impact on O3 is lower and more heterogeneous. In many cities, O3 levels slightly increased (likely due to a reduced titration by NO), but these increments often remain within the (95% confidence level) uncertainties of our methodology. However, during the most stringent phase of the lockdown (beginning of April and the few following days), a clearer O3 increase is found, reaching the statistical significance in several Spanish cities (e.g. Albacete, Barcelona, Castellón, Mallorca, Murcia, Málaga).</p><p>These results are of strong interest for quantifying the corresponding health impacts of these AQ changes, especially for showing the potential trade-offs between health benefits induced by the reduction of NO2 and enhanced mortality due to higher O3.</p><p>Petetin, H., Bowdalo, D., Soret, A., Guevara, M., Jorba, O., Serradell, K., and Pérez García-Pando, C.: Meteorology-normalized impact of the COVID-19 lockdown upon NO<sub>2</sub> pollution in Spain, Atmos. Chem. Phys., 20, 11119–11141, https://doi.org/10.5194/acp-20-11119-2020, 2020.</p>

Gene–environment interactions increase the risk of pediatric-onset multiple sclerosis associated with ozone pollution

2022 ◽  
pp. 135245852110699
Author(s):  
Amin Ziaei ◽  
Amy M Lavery ◽  
Xiaorong MA Shao ◽  
Cameron Adams ◽  
T Charles Casper ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Air Pollutant ◽  
Ozone Exposure ◽  
County Level ◽  
Ozone Pollution ◽  
Ozone Level ◽  
Additive Interaction ◽  
Gene Environment ◽  
Increased Risk ◽  
Pediatric Onset

Background: We previously reported a relationship between air pollutants and increased risk of pediatric-onset multiple sclerosis (POMS). Ozone is an air pollutant that may play a role in multiple sclerosis (MS) pathoetiology. CD86 is the only non-HLA gene associated with POMS for which expression on antigen-presenting cells (APCs) is changed in response to ozone exposure. Objectives: To examine the association between county-level ozone and POMS, and the interactions between ozone pollution, CD86, and HLA- DRB1*15, the strongest genetic variant associated with POMS. Methods: Cases and controls were enrolled in the Environmental and Genetic Risk Factors for Pediatric MS study of the US Network of Pediatric MS Centers. County-level-modeled ozone data were acquired from the CDC’s Environmental Tracking Network. Participants were assigned ozone values based on county of residence. Values were categorized into tertiles based on healthy controls. The association between ozone tertiles and having MS was assessed by logistic regression. Interactions between tertiles of ozone level and the GG genotype of the rs928264 (G/A) single nucleotide polymorphism (SNP) within CD86, and the presence of DRB1*15:01 ( DRB1*15) on odds of POMS were evaluated. Models were adjusted for age, sex, genetic ancestry, and mother’s education. Additive interaction was estimated using relative excess risk due to interaction (RERI) and attributable proportions (APs) of disease were calculated. Results: A total of 334 POMS cases and 565 controls contributed to the analyses. County-level ozone was associated with increased odds of POMS (odds ratio 2.47, 95% confidence interval (CI): 1.69–3.59 and 1.95, 95% CI: 1.32–2.88 for the upper two tertiles, respectively, compared with the lowest tertile). There was a significant additive interaction between high ozone tertiles and presence of DRB1*15, with a RERI of 2.21 (95% CI: 0.83–3.59) and an AP of 0.56 (95% CI: 0.33–0.79). Additive interaction between high ozone tertiles and the CD86 GG genotype was present, with a RERI of 1.60 (95% CI: 0.14–3.06) and an AP of 0.37 (95% CI: 0.001–0.75) compared to the lowest ozone tertile. AP results indicated that approximately half of the POMS risk in subjects can be attributed to the possible interaction between higher county-level ozone carrying either DRB1*15 or the CD86 GG genotype. Conclusions: In addition to the association between high county-level ozone and POMS, we report evidence for additive interactions between higher county-level ozone and DRB1*15 and the CD86 GG genotype. Identifying gene–environment interactions may provide mechanistic insight of biological processes at play in MS susceptibility. Our work suggests a possible role of APCs for county-level ozone-induced POMS risk.

scholarly journals Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1070 ◽  
Author(s):  
Liguang Li ◽  
Ziqi Zhao ◽  
Hongbo Wang ◽  
Yangfeng Wang ◽  
Ningwei Liu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Air Pollutants ◽  
Correlation Coefficients ◽  
Air Pollutant ◽  
Liaoning Province ◽  
Industrial Zone ◽  
Residential Areas ◽  
Pollutant Concentrations ◽  
Functional Zones ◽  
Using Data

Air pollution is a critical urban environmental issue in China; however, the relationships between air pollutants and ecological functional zones in urban areas are poorly understood. Therefore, we analyzed the spatiotemporal characteristics of four major air pollutants (particulate matter less than or equal to 2.5 µm (PM2.5) and 10 µm (PM10) in diameter, SO2, and NO2) concentrations over five ecological functional zones in Shenyang, Liaoning Province, at hourly, seasonal, and annual scales using data collected from 11 monitoring stations over 2 years. We further assessed the relationships between these pollutants and meteorological conditions and land-use types at the local scale. Peaks in PM, SO2, and NO2 concentrations occurred at 08:00–09:00 and 23:00 in all five zones. Daytime PM concentrations were highest in the industrial zone, and those of SO2 and NO2 were highest in residential areas. All four air pollutants reached their highest concentrations in winter and lowest in summer. The highest mean seasonal PM concentrations were found in the industrial zone, and the highest SO2 and NO2 concentrations were found in residential areas. The mean annual PM and SO2 concentrations decreased in 2017 in all zones, while that of NO2 increased in all zones excluding the cultural zone. The natural reserve zone had the lowest concentrations of all pollutants at all temporal scales. Pollutant concentrations of PM2.5, PM10, SO2, and NO2 were correlated with visibility, and their correlation coefficients are 0.675, 0.579, 0.475, and 0.477. Land coverage with buildings and natural vegetation negatively and positively influence air pollutant concentrations, respectively.

scholarly journals Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 504
Author(s):  
Said Munir ◽  
Gulnur Coskuner ◽  
Majeed Jassim ◽  
Yusuf Aina ◽  
Asad Ali ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Human Life ◽  
Additive Model ◽  
Positive Association ◽  
Air Pollutant ◽  
Back Trajectory ◽  
Generalised Additive Model ◽  
Mobility Data ◽  
The Impact

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

scholarly journals Spatial modelling of air pollution in urban areas with GIS: a case study on integrated database development

2005 ◽  
Vol 4 ◽  
pp. 63-68 ◽  
Author(s):  
L. Matejicek
Keyword(s):  
Air Pollution ◽  
Data Management ◽  
Spatial Data ◽  
Urban Areas ◽  
Satellite Images ◽  
Spatial Models ◽  
Physical Modelling ◽  
Air Pollutant ◽  
Automatic Monitoring System ◽  
Wide Range

Abstract. A wide range of data collected by monitoring systems and by mathematical and physical modelling can be managed in the frame of spatial models developed in GIS. In addition to data management and standard environmental analysis of air pollution, data from remote sensing (aerial and satellite images) can ehance all data sets. In spite of the fact that simulation of air pollutant distribution is carried out by standalone computer systems, the spatial database in the framework of the GIS is used to support decision-making processes in a more efficient way. Mostly, data are included in the map layers as attributes. Other map layers are carried out by the methods of spatial interpolation, raster algebra, and case oriented analysis. A series of extensions is built into the GIS to adapt its functionality. As examples, the spatial models of a flat urban area and a street canyon with extensive traffic polluted with NOx are constructed. Different scales of the spatial models require variable methods of construction, data management, and spatial data sources. The measurement of NOx and O3 by an automatic monitoring system and data from the differential absorption LIDAR are used for investigation of air pollution. Spatial data contain digital maps of both areas, complemented by digital elevation models. Environmental analyses represent spatial interpolations of air pollution that are displayed in horizontal and vertical planes. Case oriented analyses are mostly focused on risk assessment methods. Finally, the LIDAR monitoring results and the results obtained by modelling and spatial analyses are discussed in the context of environmental management of the urban areas. The spatial models and their extensions are developed in the framework of the ESRI's ArcGIS and ArcView programming tools. Aerial and satellite images preprocessed by the ERDAS Imagine represent areas of Prague.

Differences in foliar endophyte communities of red alder (Alnus rubra) exposed to varying air pollutant levels

Botany ◽  
2018 ◽  
Vol 96 (12) ◽  
pp. 825-835 ◽  
Author(s):  
Emily R. Wolfe ◽  
Stefanie Kautz ◽  
Sebastian L. Singleton ◽  
Daniel J. Ballhorn
Keyword(s):  
Pacific Northwest ◽  
Urban Areas ◽  
Land Use Changes ◽  
Fungal Endophyte ◽  
Air Pollutant ◽  
Deciduous Tree ◽  
Alnus Rubra ◽  
Ozone Pollution ◽  
Red Alder ◽  
Endophyte Community

In the Pacific Northwest, Alnus rubra Bong. (red alder) is a common deciduous tree species especially prevalent in riparian corridors and disturbed sites, including metropolitan areas undergoing land use changes and development. Importantly, red alder is also considered a bioindicator for ozone pollution and, like all plants, harbors a diverse endophyte community that may interact with aerial pollutants. In this study, we surveyed foliar fungal endophyte communities (microfungi) in red alder leaves from the metropolitan area of Portland, Oregon, USA, using culture-based techniques, and found that communities differed significantly by site. Our results suggest that the fungal endophyte community composition in red alder leaves may be influenced in part by local air pollution sources, likely in conjunction with other site characteristics. As urban areas expand, more studies should focus on how the urban environment affects plant–microbe community ecology and endophyte–host interactions, as well as on the long-term consequences for other ecosystem processes such as leaf litter decomposition.

scholarly journals Multitemporal analysis with statistical models: influence of the atmospheric condition on urban concentrations of particulate matter

2022 ◽  
Vol 2159 (1) ◽  
pp. 012003
Author(s):  
L Rodríguez-Garavito ◽  
K J Romero-Corredor ◽  
C A Zafra-Mejía
Keyword(s):  
Particulate Matter ◽  
Statistical Models ◽  
Urban Areas ◽  
Moving Average ◽  
Atmospheric Condition ◽  
Air Pollutant ◽  
Atmospheric Instability ◽  
Pollutant Concentrations ◽  
Multitemporal Analysis ◽  
Air Pollutant Concentrations

Abstract This paper shows a multitemporal analysis with autoregressive integrated moving average models of the influence of atmospheric condition on concentrations of particulate matter ≤ 10 µm in Bogotá city, Colombia. Information was collected from six monitoring stations distributed throughout the city. The study period was nine years. Autoregressive component of the models suggests that urban areas with greater atmospheric instability show a lower hourly persistence of particulate matter (one hour) compared to urban areas with lower atmospheric instability (two hours). Moving average component of the models hints those urban areas with greater atmospheric instability show greater hourly variability in particulate matter concentrations (5-10 hours). The models also suggest that a high degree of air pollution decreases the temporal influence of the atmospheric condition on particulate matter concentrations; in this case, the temporal behavior of particulate matter possibly depends on the urban emission sources of this pollutant rather than on the existing atmospheric condition. This study is relevant to deepen the knowledge in relation to the following aspects of atmospheric physics: The use of statistical models for the time series analysis of atmospheric condition, and the analysis by statistical models of the influence of atmospheric condition on air pollutant concentrations.

Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

2017 ◽  
Vol 9 (3) ◽  
pp. 13-35 ◽  
Author(s):  
Lazaros Iliadis ◽  
Vardis-Dimitris Anezakis ◽  
Konstantinos Demertzis ◽  
Georgios Mallinis
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Urban Areas ◽  
Respiratory Diseases ◽  
Air Pollutant ◽  
Chi Square ◽  
Self Organizing Maps ◽  
Pollutant Concentrations ◽  
Chi Square Test ◽  
The Impact

During the last few decades, climate change has increased air pollutant concentrations with a direct and serious effect on population health in urban areas. This research introduces a hybrid computational intelligence approach, employing unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory diseases of citizens. The system is entitled Air Pollution Climate Change Cardiovascular and Respiratory (APCCCR) and it combines the fuzzy chi square test (FUCS) with the UML self organizing maps algorithm. A major innovation of the system is the determination of the direct impact of air pollution (or of the indirect impact of climate change) to the health of the people, in a comprehensive manner with the use of fuzzy linguistics. The system has been applied and tested thoroughly with spatiotemporal data for the Thessaloniki urban area for the period 2004-2013.

Variation of Indoor Air Quality among Urban Public Venues in Kaohsiung City, Taiwan

2013 ◽  
Vol 838-841 ◽  
pp. 1928-1933
Author(s):  
Mei Fang Lu ◽  
Jim Jui Min Lin
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Urban Areas ◽  
Air Pollutant ◽  
High Concentration ◽  
Direct Reading ◽  
Pollutant Concentrations ◽  
High Concentrations

Modern people spent nearly 90% of their time indoor, and therefore, indoor air quality can directly affect our health. Recently, air quality has been much more emphasized than ever. Since 1970s, many studies have shown that the indoor air quality (IAQ) at urban areas can be worse than the outdoor air quality. To investigate the current condition of urban indoor air quality as well as differences between various types of public venues, this study used either a direct-reading instrument or the method proposed by the Taiwan Environmental Administration (TWEPA) for monitoring indoor air quality. The acquired data were used for variation analysis of indoor air quality of different public venues. Fifteen public venues were selected in this study for monitoring their indoor air quality, and among these 15 public venues, four of them are Category I venues, while the remaining eleven are Category II venues. The measurement was conducted twice at each of the public venue, and the monitored pollutants are CO2, CO, HCHO, TVOC, O3, PM10, PM2.5, bacteria, fungi, humidity, and temperature. The results suggested that there were nine public venues in the first sampling and seven public venues in the second sampling with indoor air pollutant concentrations exceeding the standards. Furthermore, the concentrations of CO2, CO, and O3, from the two measurements exceeded the indoor air quality standards. Therefore, CO2, CO, and O3can be considered as the main contributors to the poor indoor air quality of public venues. The high concentrations of CO2and O3indoor are related to population density and the use of O3generating machines, printers, or photocopiers. As for CO, the high concentration is because of the spread of exhaust gases from vehicles or factories may penetrate from outside to the indoor.

scholarly journals Mobile-Platform Measurement of Air Pollutant Concentrations in California: Performance Assessment, Statistical Methods for Evaluating Spatial Variations, and Spatial Representativeness

2020 ◽  
Author(s):  
Paul A. Solomon ◽  
Dena Vallano ◽  
Melissa Lunden ◽  
Brian LaFranchi ◽  
Charles L. Blanchard ◽  
...  
Keyword(s):  
Los Angeles ◽  
San Francisco ◽  
Urban Areas ◽  
Spatial Variations ◽  
Air Pollutant ◽  
Mobile Platform ◽  
Pollutant Concentrations ◽  
Accuracy And Precision ◽  
Research Grade ◽  
Air Pollutant Concentrations

Abstract. Mobile platform measurements provide new opportunities for characterizing spatial variations of air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc. mobile measurement and data acquisition platform was used to equip Google Street View cars with research-grade instruments. On-road measurements of air quality were made between May 2016 and September 2017 at high (i.e., 1-second [s]) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including non-urban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations of air pollutant concentrations over measurement periods as short as two weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments in stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), methane (CH4) black carbon (BC), and particle number (PN) concentration with bias and precision ranging from

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