Air Pollution Variables Research Articles

Analysing the Evidence of the Effects of Climate Change, Air Pollutants, and Occupational Factors in the Appearance of Cataracts

Cataracts are ocular conditions characterized by the opacification of the natural lens within the eye, which develops gradually over time and can affect one or both eyes. This condition commonly results from age-related changes in the lens, but can also arise from various factors. Cataract surgeries are expensive, particularly in states such as Spain, where they receive full support from the Spanish social welfare system. Despite a significant body of research on cataracts, few studies address the social and environmental factors triggering their development or consider the spatiotemporal evolution of their impacts. We analysed the incidence of cataracts in a southern region of Spain, differentiating between senile cataracts (those over 60 years old) and early cataracts (those between 15 and 59 years old). Twenty-one socio-economic, climate, and air pollution variables were statistically analysed using bivariate correlation, cluster analysis, and Geographic Information Systems. Eleven years of observation show a decadal increase in annually averaged maximum temperature and a decrease in annual precipitation, partially explaining the rising incidence of operable cataracts in the following year (r = 0.77 and −0.84, respectively; p < 0.05). Furthermore, early cataracts responded spatially to % agricultural employment (r = 0.85; p < 0.05) and moderately to maximum temperatures, insolation, and various constituents.

Modeling the Impact of Air Pollution and Meteorological Variables on COVID-19 Transmission in Western Cape, South Africa

Understanding the factors that influence COVID-19 transmission is essential in assessing and mitigating the spread of the pandemic. This study focuses on modeling the impact of air pollution and meteorological parameters on the risk of COVID-19 transmission in Western Cape Province, South Africa. The data used in this study consist of air pollution parameters, meteorological variables, and COVID-19 incidence observed for 262 days from April 26, 2020, to January 12, 2021. Lagged data were prepared for modeling based on a 6-day incubation period for COVID-19 disease. Based on the overdispersion property of the incidence, negative binomial (NB) and generalised Poisson (GP) regression models were fitted. Stepwise regression was used to select the significant predictors in both models based on the Akaike information criterion (AIC). The residuals of both NB and GB regression models were autocorrelated. An autoregressive integrated moving average (ARIMA) model was fitted to the residuals of both models. ARIMA (7, 1, 5) was fitted to the residuals of the NB model while ARIMA (1, 1, 6) was fitted for the residuals of the GP model. NB + ARIMA (7, 1, 5) and GP + ARIMA (1, 1, 6) models were tested for performance using root mean square error (RSME). GP + ARIMA (1, 1, 6) was selected as the optimal model. The results from the optimal model suggest that minimum temperature, ambient relative humidity, ambient wind speed, PM2.5, and NO2 at various lags are positively associated with COVID-19 incidence while maximum relative humidity, minimum relative humidity, solar radiation, maximum temperature, NO, PM load, PM10, SO2, and NOX at various lags have a negative association with COVID-19 incidence. Ambient wind direction and temperature showed a nonsignificant association with COVID-19 at all lags. This study suggests that meteorological and pollution parameters play a vital independent role in the transmission of the SARS-CoV-2 virus.

Long-term variations of air pollutants and public exposure in China during 2000–2020

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000–2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Seasonal Variations Assessment of Air Pollutants of Communities in the Vicinity of Scrap Metal Recycling Industries in Ogijo, Shagamu South LGA, Ogun State, SW Nigeria

This manuscript presents a thorough investigation into the seasonal variations of air pollutants in communities surrounding scrap metal recycling industries in Ogijo, Shagamu South LGA, Ogun State, Nigeria utilizing advanced Gary Wolf Environmental Sensing and Particulate Counting devices. The study meticulously measures concentrations of carbon monoxide, nitrogen dioxide, sulfur dioxide, PM2.5, and PM10 during both dry and wet seasons across 20 strategic sampling locations and control. The findings reveal significant seasonal fluctuations in pollutant levels, with some concentrations exceeding Nigerian ambient air quality standards, highlighting a pressing environmental health concern. The study not only underscores the critical need for regulatory oversight and the implementation of safer metal scrapping practices but also advocates for regular environmental monitoring to mitigate the adverse impacts of such industries on local communities and the environment.

Temporal Variations in Urban Air Pollution during a 2021 Field Campaign: A Case Study of Ethylene, Benzene, Toluene, and Ozone Levels in Southern Romania

This study focused on quantifying the gas concentrations of ethylene, benzene, toluene, and ozone within an urban area in the southern region of Romania. The gas sampling campaign, conducted between March and August 2021, took place in three different locations from the point of view of the architectural structure, and the sampling height was 1.5 m. Sampling occurred on weekdays (Monday through Friday) during daylight hours, with subsequent concentration analysis employing descriptive statistics, diurnal cycles, and seasonal assessments. A highly sensitive and selective detector, employing laser photoacoustic spectroscopy, was utilized to monitor pollutants. The average concentrations (±Standard Deviation) were determined as follows: ethylene at 116.82 ± 82.37 parts per billion (ppb), benzene at 1.13 ± 0.32 ppb, toluene at 5.48 ± 3.27 ppb, and ozone at 154.75 ± 68.02 ppb, with peak levels observed during the summer months. Diurnal patterns were observable for ethylene, benzene, and toluene, exhibiting higher concentrations during the early hours of the day followed by a decrease towards the evening. In contrast, ozone concentrations peaked in the evening compared to the early part of the day. Thus, perceptible effects were demonstrated on gas concentrations as a result of the influence of meteorological variables. Moreover, the high toluene/benzene ratio indicated traffic and industrial emissions as primary sources of these pollutants. Of the four gases monitored, benzene and ozone exceeded regulatory limits, particularly during the summer season, highlighting concerns regarding air quality in the studied urban environment.

Spatial-temporal distribution and emission of urban scale air pollutants in Hefei based on Mobile-DOAS

As a significant city in the Yangtze River Delta regions, Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion. However, there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants. To address this, this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022. The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data. Temporally, there were pronounced seasonal variations in air pollutants. Spatially, high concentration of HCHO and NO2 were closely associated with traffic congestion on roadways, while heightened SO2 levels were attributed to winter heating and industrial emissions. The study also revealed that with the implementation of road policies, the average vehicle speed increased by 95.4%, while the NO concentration decreased by 54.4%. In the estimation of urban NOx emission flux, it was observed that in temporal terms, compared with inventory data, the emissions calculated via mobile measurements exhibited more distinct seasonal patterns, with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer. In spatial terms, the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city's primary NOx emission sources in the area between these two rings. This study offers data support for formulating the next phase of air pollution control measures in urban areas.

Impact of atmospheric O3 and NO2 on the secondary sulfates in real atmosphere

As an important component of secondary aerosols, sulfate plays a crucial role in regulating atmospheric radiative balance and influencing the secondary formation of ozone (O3). In real atmosphere, atmospheric oxidants NO2 and O3 can promote the oxidation of SO2 to form sulfate (SO42−) through multiphase chemistry that occur at different time scales. Due to the combined impact of meteorology, pollution sources, atmospheric chemistry, etc., time-scale dependence of SO2-SO42− conversion makes the impact of NO2/O3 on it more complex. In this study, based on long-term time series (2013-2020) of air pollution variables from seven stations in Hong Kong, the Multifractal Detrended Cross-Correlation Analysis (MFDCCA) method has been employed to quantify the cross-correlations between SO2 and SO42− in real atmosphere at different time scales, for examining the time-scale dependence of SO2-SO42− conversion efficiency. Furthermore, the Pearson correlation analysis has been used to study the influence of NO2/O3 on SO2-SO42− conversion, and the regional and seasonal differences have been analyzed by considering factors such as meteorology, pollution sources, and regional transport. Changes in the main components of secondary aerosols are closely linked with the co-control of regional PM2.5 and O3. Therefore, the exploration of the impact of co-existing NO2/O3 gases on the secondary formation of sulfates in real atmosphere is significant.

Modelling spatial & temporal variability of air pollution in an area of unconventional natural gas operations

Despite the growing unconventional natural gas production industry in northeastern British Columbia, Canada, few studies have explored the air quality implications on human health in nearby communities. Researchers who have worked with pregnant women in this area have found higher levels of volatile organic compounds (VOCs) in the indoor air of their homes associated with higher density and closer proximity to gas wells. To inform ongoing exposure assessments, this study develops land use regression (LUR) models to predict ambient air pollution at the homes of pregnant women by using natural gas production activities as predictor variables. Using the existing monitoring network, the models were developed for three temporal scales for 12 air pollutants. The models predicting monthly, bi-annual, and annual mean concentrations explained 23%–94%, 54%–94%, and 73%–91% of the variability in air pollutant concentrations, respectively. These models can be used to investigate associations between prenatal exposure to air pollutants associated with natural gas production and adverse health outcomes in northeastern British Columbia.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany)

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone (O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document}), nitrogen dioxide (NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}), particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{2.5}$$\\end{document}, PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{10}$$\\end{document}) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (−0.84), April (−0.47), and October (−0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document} and NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}. Spatially, NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}, PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{2.5}$$\\end{document}, and PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{10}$$\\end{document} concentrations were significantly higher in urban than rural regions. For O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document}, this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM2.5), a significant portion of ambient PM2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM2.5, are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM2.5 both temporally and spatially. Monthly network-average BC was 3 × higher in winter than late spring, while PM2.5 was only 10% lower. In both seasons, average PM2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM2.5, and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM2.5. Thus, including BC in addition to PM2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

Persepsi Masyarakat mengenai Pengaruh Pengembangan Wisata Pantai terhadap Lingkungan: Studi Kasus di Kabupaten Pinrang, Indonesia

Beach tourism in Pinrang Regency continues to develop. This tourism development has impact on various aspects, one of which is the environmental aspect. This research aims to analyze public perceptions regarding the effect of beach tourism development on the environment in Pinrang Regency. Public perceptions data were collected through filling out questionnaires and direct interviews. Apart from that, secondary data collection from local stakeholders was also carried out. The questionnaire was first analyzed for validity and reliability before being used. The data collected was then analyzed descriptively. Public perceptions based on gender and education level were analyzed for differences using non-parametric tests (Kruskal-Wallis H). The results of this research showed that the development of beach tourism in Pinrang Regency did not have a bad effect on the environment based on public perceptions. There was a significant difference in men's and women's perceptions of air pollution variables (p&lt;0.05). Apart from that, there were significant differences in public perceptions on the vandalism, air pollution, noise pollution, narrowing of public spaces, and damage to plants variables based on education level (p&lt;0.05). It is hoped that the results of this research will provide information for stakeholders in managing beach tourism better and improving the welfare of the surrounding community, especially in Pinrang Regency

The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use.

Rationale: The share of Black or Latinx residents in a census tract remains associated with asthma-related emergency department (ED) visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to the within-city heterogeneity of long-term air pollution exposure remains unclear. Objectives: To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. Methods: An administrative database was used to define census tract population-based incidence rates of asthma-related ED visits. We estimate the associations between census tract incidence rates and 1) average fine and coarse particulate matter, nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) racial and ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We also examine for the attenuation of incidence risk ratios (IRRs) associated with race/ethnicity when controlling for air pollution exposure. Measurements and Main Results: Fine and coarse particulate matter and SO2 are all associated with census tract-level incidence rates of asthma-related ED visits, and multipollutant models show evidence of independent risk associated with coarse particulate matter and SO2. The association between census tract incidence rate and Black resident share (IRR, 1.51 [credible interval (CI), 1.48-1.54]) is attenuated by 24% when accounting for air pollution (IRR, 1.39 [CI, 1.35-1.42]), and the association with Latinx resident share (IRR, 1.11 [CI, 1.09-1.13]) is attenuated by 32% (IRR, 1.08 [CI, 1.06-1.10]). Conclusions: Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study.

A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7days. Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China

Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015–2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.

Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation

In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25–30 °C, and humidity levels of 75 %–85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts.

Underappreciated Emission Spikes From Power Plants During Heatwaves Observed From Space: Case Studies in India and China

AbstractThe frequency, intensity, and duration of extreme heatwaves are projected to increase in the global context of climate change. However, evidence of how anthropogenic emissions respond to heatwaves and further impact air quality remains elusive. Here, we use satellite remote sensing measurements alongside chemical transport model simulations to reveal abrupt variations in primary and secondary air pollutants introduced by extreme heatwaves. We highlight evidence from China and India, where satellite sulfur dioxide (SO2) and nitrogen dioxide (NO2) columns over thermal power plants enhance consistently responding to heatwaves. We attribute such spiked emissions to soaring electricity use and demonstrate that bottom‐up inventories underestimate the emissions from the power sector by 34.9% for the selected case. Elevated emissions facilitate fine particulate matter (PM2.5) and ozone (O3) formation over thermal power plants in an inhomogeneous manner, due to the combined effect of atmospheric oxidizing capacity, thermal decomposition of peroxyacetyl nitrate, planetary boundary layer rise, and air stagnation. Our results underscore the emerging challenge of pollution control attributable to the increasing climate penalty and the necessity of targeted control strategies and alternative energy sources during heatwaves.

The price of clean air – quantifying air pollution exposure in real estate decisions

PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning.

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.

Neighborhood-Level Air Pollution Monitoring and Analysis in Massachusetts

Air pollution is a significant environmental issue with far-reaching consequences for both human health and the planet. It arises from the emission of harmful pollutants, such as particulate matter, ozone, and nitrogen dioxide, into the atmosphere. These pollutants originate from various sources, including vehicles, industrial facilities, and power plants. The impacts of air pollution are wide-ranging, encompassing respiratory infections, cardiovascular disease, cancer, harm to ecosystems, and contributions to climate change. The study of air pollution is a multidisciplinary field that draws upon knowledge from physics, chemistry, biology, and engineering. Understanding the causes and effects of air pollution is crucial for developing effective strategies to mitigate its harmful effects. Physics, in particular, plays a vital role in this field by providing the tools and techniques required to measure and comprehend the behavior of pollutants. By examining air pollution, we gain insights into the factors driving this problem and can devise measures to reduce pollution levels. Moreover, this knowledge empowers individuals and societies to make informed decisions about minimizing exposure to pollutants and formulating policies and regulations that safeguard both human well-being and the environment. In our study conducted in the central Massachusetts region, we investigated the status of air pollution using a combination of Environmental Protection Agency (EPA) monitoring sites and hand-held sensors. While the EPA sites offer longterm monitoring data, hand-held devices’ flexible and affordable nature allowed us to explore air quality at the local neighborhood and street levels. By utilizing these tools, we assessed the spatial and temporal variations in air pollution within the city, aiding in the identification of localized hotspots. Such information is valuable for targeting specific areas requiring interventions and further understanding the dynamics of pollution distribution.

How urban air quality affects land values: Exploring non-linear and threshold mechanism using explainable artificial intelligence

Air pollution has a significant impact on human health and influences housing choices. Existing studies on determinant factors affecting property prices, including air pollution, mainly employed hedonic and spatial regression models that have limitations in capturing non-linear relationships and local interactions. Neglecting the non-linear relationships can lead to incomplete estimation impacts between explainable features and prices. To fill these gaps, this study used machine learning algorithms and SHAP (SHapley Additive exPlanations) to identify the relative importance of air pollutant variables on land values and their non-linear mechanism. The results showed that the Extreme Gradient Boosting (XGBoost) outperformed Ordinary Least Squares (OLS), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Extra Tree Regression (ETR) models in price prediction and capturing the non-linear relationship between air pollution and land values. Notably, Ozone (O3) and Nitric Oxide (NO) concentrations contribute 1.64% and 1.47% to land value variation, respectively. The results confirmed the non-linear relationship and identified that the threshold effect between air pollution and land values is at mean concentration values. Furthermore, we observed that the negative influence of O3 and NO on land values appears as their concentration level is higher than 29.3 and 13 ppb, respectively. This paper contributes valuable insights by advancing our understanding of the non-linear mechanism impact of air pollution on house and land values, informing environmental policymaking, and shedding light on housing decisions.