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Monitoring air pollution is important for human health and the environment. Previous studies on the prediction of air pollutants from satellite images have employed machine learning, yet there are few enhancements to the constructure of model. Moreover, the existing models have been successful in predicting pollutants like PM2.5, PM10, and O3. They have not been as effective in predicting other primary air pollutants. To improve the overall prediction performance of the existing model, a novel residual learning backpropagation model, abs. as BresNet, has been proposed in this research. This model has revealed the availability to precisely predict the ground-surface concentration of the six primary air pollutants, PM2.5, PM10, O3, NO2, CO, and SO2, based on the satellite imagery of MODIS AOD. Two of the most commonly used machine learning models so far, viz. the multilayer backpropagation neural network (MLBPN) and random forest (RF), were employed as the control. In the conducted experiments, the proposed BresNet model demonstrated significant improvements of 18.75%/31.94%, 33.82%/85.71%, 15.00%/35.29%, 39.06%/134.21%, 23.23%/68.00%, and 137.14%/260.87% in terms of R2 for the six primary air pollutants, compared to the RF/MLBPN model. Moreover, the convergence speed and loss function of the BresNet model compared to that of the MLBPN decreased by 55.15%, revealing superior convergence speed with the lower loss function. [ABSTRACT FROM AUTHOR]

Main conclusion: Ambient concentrations of atmospheric nitrogen dioxide (NO2) inhibit the binding of PIF4 to promoter regions of auxin pathway genes to suppress hypocotyl elongation in Arabidopsis. Ambient concentrations (10–50 ppb) of atmospheric nitrogen dioxide (NO2) positively regulate plant growth to the extent that organ size and shoot biomass can nearly double in various species, including Arabidopsis thaliana (Arabidopsis). However, the precise molecular mechanism underlying NO2-mediated processes in plants, and the involvement of specific molecules in these processes, remain unknown. We measured hypocotyl elongation and the transcript levels of PIF4, encoding a bHLH transcription factor, and its target genes in wild-type (WT) and various pif mutants grown in the presence or absence of 50 ppb NO2. Chromatin immunoprecipitation assays were performed to quantify binding of PIF4 to the promoter regions of its target genes. NO2 suppressed hypocotyl elongation in WT plants, but not in the pifq or pif4 mutants. NO2 suppressed the expression of target genes of PIF4, but did not affect the transcript level of the PIF4 gene itself or the level of PIF4 protein. NO2 inhibited the binding of PIF4 to the promoter regions of two of its target genes, SAUR46 and SAUR67. In conclusion, NO2 inhibits the binding of PIF4 to the promoter regions of genes involved in the auxin pathway to suppress hypocotyl elongation in Arabidopsis. Consequently, PIF4 emerges as a pivotal participant in this regulatory process. This study has further clarified the intricate regulatory mechanisms governing plant responses to environmental pollutants, thereby advancing our understanding of how plants adapt to changing atmospheric conditions. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with a miniaturized sensor package were developed for aerial observations, which realizes aerial observations affordable to scientists in atmospheric science and achieves aerial measurements in high spatial resolution. UAVs are deployed to a variety of aerial detecting tasks in different scientific scenarios including chemical industry parks (CIPs) with hazardous gases emissions, and some places difficult for humans to reach. In this study, UAV sensing technology was deployed to detect air pollutants in a suburb, a CIP and a natural gas plant, respectively. The effects of atmospheric conditions such as the atmospheric boundary layer height, long-distance transport and atmospheric stability on the spatiotemporal variations of the air pollutants vertical profiles were investigated by the UAV. The UAV with the sensor package was deployed to capture the methane (CH4) leakages in a natural gas plant. The spatiotemporal variations of CH4 in both vertical and horizontal directions studied by UAV were employed to calculate accurate CH4 emissions, which is crucial to reducing the emissions of greenhouse gases. The low-cost UAV sensing technology for air pollutants was developed by Dr. Xiaobing Pang, who was funded by the Newton Fellowship in 2009 and worked in the University of York. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

Abstract Background Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. Method Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. Results A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507–0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116–1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425–0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135–0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322–0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells

Exposure to ambient air pollution has serious adverse impacts on human health. Yet air pollution does not affect all individuals in the same way. Existing evidence of environmental inequality in low- and middle-income countries (LMIC) is limited and contradictory, despite 91% of premature death due to air pollution in LMIC. This study aims to estimate the association between community socioeconomic status (CSES) and ambient air pollution in eastern China. The study comprised 19,622 individuals. CSES was measured by income, occupation and education. Air pollution was measured by 4-year-average ambient levels of PM2.5, PM10, NO2, and CO. Spatial autoregressive models and U-test was applied. Subsample analyses were conducted based on participants’ rural/urban location and hukou status. Air pollutant exposure had an inverted U-shaped correlation with CSES. Before (after) the inflection point, increasing CSES by 1% increased(decreased) community exposure to PM2.5 by 0.527% (0.379%), PM10 by 0.460%(0.215%), NO2 by 0.584%(0.288%), and CO by 0.582% (0.382%). All results remained robust in sensitivity analysis. Subsample analysis showed that compared to rural (urban) residents, the increment of air pollution exposure concentration for migrants was 4.042 (4.556) μg/m3 for PM2.5, 5.839 (10.624) μg/m3 for PM10, 3.212 (5.719) μg/m3 for NO2 and 0.205(0.208) mg/m3 for CO. Our study finds moderate SES communities facing the highest level of exposure. Our results aid policymakers to understand the locality-specific patterns of environmental pollution and to design intervention strategies to improve the environment, especially for economically vulnerable groups, such as migrants. [ABSTRACT FROM AUTHOR]

The present investigation proposes a Bayesian Regularized Artificial Neural Network (BRANN) based multi input single output (MISO) soft sensor. It predicts the value of air quality index (AQI) by receiving measured inlet concentration of different air pollutants such as particulate matter-2.5 (PM2.5), particulate matter-10 (PM10), SO2, NO2, O3, and CO. The performance of BRANN is also evaluated comparing with other two soft sensors namely, scaled conjugate gradient trained artificial neural network (SCGANN) and Levenberg-Marquardt artificial neural network (LMANN). The Bayesian Regularization (trainbr), trainscg, and Levenberg-Marquardt (LM) feed-forward backpropagation algorithms are used to train BRANN, SCGANN and LMANN, respectively. Hence, for their training and validation, two sets of 24 hours average of real time data released by the Central Pollution Control Board (CPCB) of India for Talcher coalfield and its surrounding area in Odisha from Jan-2019 to Dec-2020 are used. The training performances of all three soft sensors are reasonable (average R2 values of 0.975452, 0.995533 and 0.998718 for SCGANN, LMANN and BRANN, respectively). Obviously, the LMANN exhibits its superiority over SCGANN (corresponding average R2 values of 0.811955 and 0.760921). Here, the BRANN overperforms (average R2 values of 0.891167) other two soft sensors in predicting the AQI. [ABSTRACT FROM AUTHOR]

This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM10, NO2, and PM2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. Method: Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. Results: A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507–0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116–1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425–0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135–0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322–0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. Conclusion: For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA. [ABSTRACT FROM AUTHOR]

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic's course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic's course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic's course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution. [ABSTRACT FROM AUTHOR]

Meteorological factors and air pollutants are associated with the spread of pulmonary tuberculosis (PTB), but few studies have examined the effects of their interactions on PTB. Therefore, this study investigated the impact of meteorological factors and air pollutants and their interactions on the risk of PTB in Urumqi, a city with a high prevalence of PTB and a high level of air pollution. The number of new PTB cases in eight districts of Urumqi from 2014 to 2019 was collected, along with data on meteorological factors and air pollutants for the same period. A generalized additive model was applied to explore the effects of meteorological factors and air pollutants and their interactions on the risk of PTB incidence. Segmented linear regression was used to estimate the nonlinear characteristics of the impact of meteorological factors on PTB. During 2014–2019, a total of 14,402 new cases of PTB were reported in eight districts, with March to May being the months of high PTB incidence. The exposure–response curves for temperature (Temp), relative humidity (RH), wind speed (WS), air pressure (AP), and diurnal temperature difference (DTR) were generally inverted "U" shaped, with the corresponding threshold values of − 5.411 °C, 52.118%, 3.513 m/s, 1021.625 hPa, and 8.161 °C, respectively. The effects of air pollutants on PTB were linear and lagged. All air pollutants were positively associated with PTB, except for O3, which was not associated with PTB, and the ER values for the effects on PTB were as follows: 0.931 (0.255, 1.612) for PM2.5, 1.028 (0.301, 1.760) for PM10, 5.061 (0.387, 9.952) for SO2, 2.830 (0.512, 5.200) for NO2, and 5.789 (1.508, 10.251) for CO. Meteorological factors and air pollutants have an interactive effect on PTB. The risk of PTB incidence was higher when in high Temp-high air pollutant, high RH-high air pollutant, high WS-high air pollutant, lowAP-high air pollutant, and high DTR-high air pollutant. In conclusion, both meteorological and pollutant factors had an influence on PTB, and the influence on PTB may have an interaction. [ABSTRACT FROM AUTHOR]

Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40–69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5–10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1–T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4–28.7) μg/m3; NOx, 41.3 (36.2–46.7) μg/m3; PM10, 15.9 (15.4–16.4) μg/m3; PM2.5, 9.9 (9.5–10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0–1.2) per metre; and PM2.5–10, 6.1 (5.9–6.3) μg/m3. Compared with the low group, the high group’s APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5–10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5–10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants.

Abstract Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.

BackgroundAffected by concentration, composition, and population tolerance of air pollutants, the relationship between air pollutants and population health has regional differences. There is still a research gap in Guiyang.ObjectiveTo explore the short-term effects of air pollutant concentrations in low-pollution areas on the outpatient volume of respiratory diseases.MethodsSpearman correlation analysis was used to evaluate the correlation between air pollutants, meteorological factors, and respiratory outpatient volume from January 1, 2013 to December 31, 2020 in Guiyang City. A single pollutant distribution lag nonlinear model and a multi-pollutant interaction model were established based on Poisson distribution. A three-dimensional diagram was drawn to display the relationship between air pollutants and respiratory outpatient volume. Quantitative analysis was conducted on the attribution risk and lag effect of air pollutant concentration on outpatient volume of respiratory diseases in Guiyang City.ResultsThe results of the single pollutant model showed that fine particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2) elevated the outpatient volume of respiratory diseases. The maximum relative risk (RR) and 95%CI values of PM2.5, NO2,CO, and SO2 appeared on Day 2, 0, 5, and 6, respectively, which were 1.019 (1.015, 1.023), 1.146 (1.122, 1.171), 1.129 (1.116, 1.143), and 1.046(1.040, 1.052), respectively. For every quartile concentration increment of PM2.5, NO2, CO, or SO2, the outpatient volume of respiratory diseases increased by 0.943% (0.111%, 1.782%), 4.050% (3.573%, 4.529%), 0.595% (0.317%, 0.874%), or 0.667% (0.235%, 1.100%), respectively. The maximum RR (95%CI) of O3 was 1.015 (1.007, 1.023) and appeared on Day 0. The results of multi-pollutant model showed that PM2.5, NO2, CO, SO2, and O3 all elevated the outpatient volume of respiratory diseases. The maximum RR values of PM2.5, NO2, CO, SO2 and O3 appeared on Day 14, 0, 5, 7 and 0, respectively, which were 1.027 (1.021, 1.034), 1.213 (1.179, 1.248), 1.059 (1.043, 1.074), 1.016 (1.005, 1.026), and 1.024 (1.015, 1.033), respectively. Compared with the single pollutant model, the RR values of PM2.5, NO2, and O3 on the outpatient volume of respiratory diseases in the multi-pollutant model showed an upward trend, while the RR values of CO and SO2 in the multi-pollutant model showed a downward trend.ConclusionThe impact of low concentrations of PM2.5, NO2, CO, and SO2 on human health cannot be ignored.

BackgroundThe impact of atmospheric fine particulate matter (PM2.5) and ozone (O3) on the mortality of circulatory system diseases cannot be ignored. However, whether the interaction between PM2.5 and O3 can affect population health is rarely reported and requires study. ObjectiveTo investigate the individual and interactive impacts of atmospheric PM2.5 and O3 on the mortality of circulatory system diseases in the population of Ningxia region. MethodsThe data of 119647 deaths due to circulatory system diseases, daily average concentrations of atmospheric pollutants, and meteorological data in Ningxia from 2013 to 2020 were retrieved. PM2.5 was divided into low, medium, and high concentrations according to the primary and secondary national limits (35 and 75 μg·m−3) of the Ambient air quality standards. Similarly, O3 was divided into low, medium, and high concentrations according to the national limits (100 and 160 μg·m−3). Using a generalized additive mixed model based on quasi Poisson distribution, the impacts of atmospheric PM2.5 and O3 as well as their interaction on the mortality of circulatory system diseases were analyzed using the population data of Ningxia region. ResultsDuring the target period, males and the ≥ 65 year group accounted for larger proportions of deaths due to circulatory system diseases (55.47% and 79.87% respectively). The daily average concentration of PM2.5 (40.25 μg·m−3) exceeded the national primary limit. In the single pollution model, the highest cumulative lag effects for mortality from circulatory system diseases were PM2.5 exposure over previous 1 d (lag01) and O3 exposure for previous 2 d (lag02), and their excess risk (ER) values were 1.03% (95%CI: 0.67%, 1.40%) and 1.02% (95%CI: 0.57%, 1.50%), respectively. The results of concentration stratification analysis showed that the most significant risks of death from circulatory system diseases [ER (95%CI): 1.12% (0.32%, 1.92%) and 0.95% (0.13%, 1.79%) respectively] were found at medium PM2.5 and O3 concentrations. The interaction analysis revealed that under, a synergistic effect on the risk of death from circulatory system diseases was identified (relative excess risk due to interaction=3.08%, attributable proportion of interaction=2.90%, synergy index=1.89) when considering the coexistence of PM2.5 and O3 above the primary limit. As the concentrations of PM2.5 and O3 increased, the synergistic effect increased the risk of death from circulatory system diseases in the general population, men, women, and the ≥ 65 years group. ConclusionBoth atmospheric PM2.5 and O3 can increase the risk of death from circulatory system diseases, and the two pollutants have a synergistic effect on the risk of death from circulatory system diseases.

ABSTRACTArctic miners face significant risks from diesel exhaust and dust exposure, potentially leading to adverse respiratory health. Employers must limit harmful exposures, using personal protective equipment (PPE) as a last line of defense. This study explored the association between reported respiratory exposure and symptoms, and PPE training and usage. Data from the MineHealth study (2012–2014) included a total of 453 Arctic open pit miners in Norway, Sweden, and Finland. Participants answered questions on exposure to dust and diesel exhaust, respiratory symptoms, and PPE use, in addition to age, gender, BMI, smoking, and self-rated health. Estimated exposure to dust was common, reported by 91%, 80%, and 82% and that of diesel exhaust by 84%, 43%, and 47% of workers in Sweden, Finland, and Norway, respectively. Reported dust exposure was significantly related to respiratory symptoms (OR 2.2, 95% CI 1.3–3.7), diesel exposure increased the occurrence of wheezing (OR 2.6, 95% CI 1.3–5.4). PPE use varied between the studied mines. Non-use was common and related to reduced visibility, wetness, skin irritation and fogging of the respiratory PPE. Future research should employ more precise exposure assessment, respiratory function as well as explore the reasons behind the non-compliance of PPE use.

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the "optimal" estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the "optimal" estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area. [ABSTRACT FROM AUTHOR]

Air pollution is a significant environmental problem in Isfahan City, Iran, with detrimental effects on human health and ecosystems. It comes from various sources, including the asphalt industry, motor vehicles, power plants, industry, and natural events such as forest fires and dust storms. The data used in the study was obtained through a combination of different methods and strategies. Reliable laboratory information besides reports from the European Environmental Protection Organization, American Environmental Protection Organization, and Australian Environmental Protection Organization, standards from environmental organizations were used to determine emission coefficients. Questionnaires were prepared and referred to trade and industrial units to gather information on production and air pollutant emissions. Finally, statistical analysis and grouping methods were used to analyze the data. Based on average data of emission rate of different sources, prioritizing resulted in the identification of five main sources of gaseous pollutant including chemical industries, synthetic textiles, and pharmaceutical industries (7.36%), burning garden waste outdoors (7.68), brick kilns (12.43%), garbage center (15.78), and digestion of humans (45.68). Other 30 sources contribute less than 3%. Based on the results, it is obvious that CH4 only contributes 44% in gas emissions of Isfahan City followed by CO (23%), TSP (13%), MVOCS (9%), PM10 (2.17%), NH3 (1.96%), non-methane volatile organic compounds (1.89%), Nox (1.70%), PM2.5 (1.36%), SOx (0.89%), VOC (0.76%), and PM (0.03%).The results of the paper provide information on the major sources of air pollutant emissions in Isfahan City, including chemical industries, burning garden waste outdoors, brick kilns, garbage centers, and digestion of humans. It presents the average data of emission rates from different sources, helping in prioritizing and targeting specific sectors for reducing emissions. The study highlights the importance of considering both natural and anthropogenic sources of air pollution and the need for implementing strategies to obtain accurate information on emissions. The research contributes to the understanding of air pollution sources in Isfahan City, which can aid in developing effective measures for reducing pollution and improving air quality. [ABSTRACT FROM AUTHOR]

In this study, the concentration levels of CO, NO2, CH2O, SO2, and O3 gases emitted during the two biggest wildfire episodes observed in Manavgat and Milas, Türkiye in 2021 were analyzed and spatio-temporal gas concentrations were estimated. Using the remote sensing imagery from Sentinel-5P satellite, a daily based time-series data analysis was performed over the Google Earth Engine platform (GEEp) and the gas emission levels (mol/m2) during the wildfires were obtained. The processed time-series data has been associated with the measurements from ground-stations of Türkiye National Air Quality Monitoring Network, allowing unit conversion to gas concentration unit in μg/m3. Based on predicted gas concentrations, statistical performance measurements were calculated with actual ground-station measurements. According to the spatio-temporal gas concentrations, the highest levels of CO gas emissions were detected on July 29th in Manavgat 5492.63 ± 325.12 μg/m3 and on August 5th in Milas 1071.14 ± 230.41 μg/m3. During the wildfire episodes NO2 concentration has reached to 383.52 ± 19.31 μg/m3 in Manavgat and 34.76 ± 8.20 μg/m3 in Milas. The O3 levels during the wildfires were estimated as 5.54 ± 16.09 μg/m3 in Manavgat and 41.22 ± 2.07 μg/m3 in Milas. The average SO2 concentration was 71.49 ± 4.2 μg/m3 in Manavgat and 165.35 ± 6.51 μg/m3 in Milas. Also, the average CH2O concentration was estimated as 12.83 ± 5.07 μg/m3 in Manavgat and 17.91 ± 4.41 μg/m3 in Milas. R2 values were between 0.67 and 0.84. Generally, IA values were higher than 0.70. The statistical results showed that our approach was reasonably successful in the prediction of the spatio-temporal wildfire gas emissions and can be applied to such scenarios. [ABSTRACT FROM AUTHOR]

Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza. [ABSTRACT FROM AUTHOR]

In order to explore the distribution laws of air pollutants in different regions and at different times in the northern Anhui urban agglomeration and the relevant variables affecting the concentration of PM2.5 in the air, combined with the statistical data of Anhui Provincial Department of Ecology and Environment from 2018 to 2021 and relevant regional data, the principal component analysis ( PCA) method was used to analyze the correlation on the influencing factors of air quality by selecting monthly data and annual data respectively according to the time period and seasonal changes. The changes in the concentration of air pollutants PM2.5 and PM10 and other air pollutants in different seasons were compared and analyzed, and the backpropagation neural network (BP) based on PCA algorithm was constructed. A PCA-BP model was established and a cross-validation method was used to improve the accuracy of the model, and short-term prediction of PM2.5 concentration in the atmosphere is made. The experimental results show that the main influencing factors of PM2.5 concentration are PM10, CO, NO2 and SO2 . PM2.5 content in northern Anhui is higher in winter on the whole. The accuracy of the prediction model is higher in summer and autumn, but lower in winter. The prediction accuracy R2 of the four seasons reaches 0. 924, 0. 958, 0. 935 and 0. 794, respectively. [ABSTRACT FROM AUTHOR]

This study combined the real-time monitoring and investigation of traffic flows to comprehensively analyze the road traffic flow and vehicle structure in downtown Beijing. A dynamic emission inventory of motor vehicle air pollutants in downtown Beijing in 2021 was established, and the impact of these emissions on air quality was simulated and quantified, and different emission reduction control scenarios were proposed to evaluate their environmental improvement effects and explore measures to mitigate the impact of pollution emissions. The results show that the high traffic flow and the structure of the motor vehicle emissions in downtown Beijing are the main causes of severe motor vehicle pollution. Monitoring data shows that traffic flow in central Beijing is dominated by small passenger vehicles, while the vehicle mix is better than in other regions, with 72.0% of vehicles meeting "National V" or higher emission standards. However, to achieve higher air quality goals, further reducing vehicle emissions is necessary. Based on dynamic traffic flow, the average daily emissions of nitrogen oxides (NO X), particulate matters(PM 2.5) and volatile organic compounds(VOC) from motor vehicles in central Beijing are 17.7 tons, 0.6 tons and 14.0 tons, respectively, accounting for 23.0% of the city's average daily motor vehicle emissions. If a zero-emission zone for motor vehicles were implemented in central Beijing, the annual average emission level of pollutants will be reduced by 10.4% to 21.0%. The designation of ultra-low emission zones for motor vehicles could be effective in improving the air quality in the center of Beijing. [Display omitted] [ABSTRACT FROM AUTHOR]

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The objective of this study was to develop a framework for determining the fuel use and environmental impacts caused by construction work zones (CWZs) on a range of vehicles and to produce initial calculations of these impacts by modeling traffic closure conditions for highway maintenance and rehabilitation (M&R) activities. The framework was developed and demonstrated in several scenarios. The study included three common highway categories—freeways, multi-lane highways, and two-lane highways—and common California vehicle types. The framework uses realistic drive cycle values and CWZ operation scenarios as inputs to the simulation software MOtor Vehicle Emission Simulator (MOVES) to estimate total fuel consumption and air pollutant emissions. In this study, the framework was demonstrated using three CWZ operations under different traffic congestion levels: light, medium, and heavy.Fuel consumption and pollutant emissions results obtained for the CWZ operation scenario with and without congestion were compared with those for a no-CWZ, no-congestion scenario. In the simulation results for a freeway with a CWZ and heavy congestion, fuel consumption increased by 85% and the CO2 equivalent (CO2-e) emissions increased by 86%, NOx by 62%, SOx by 85%, and PM2.5 by 128%. In the multi-lane highway scenarios, fuel consumption increased by 85%, and CO2-e emissions increased by 88%, NOx by 75%, SOx by 87%, and PM2.5 emissions by 129% for a CWZ with heavy congestion. Lessening traffic congestion in a CWZ from heavy (average speed 5 mph) to medium (average speed 25 mph for a freeway section and 15 mph for a multi-lane road section) reduced fuel consumption by 40% on a freeway and 33% on multi-lane highway.This study also included use of a pilot car in a CWZ on a two-lane road. This approach was undertaken to estimate the possible benefits of different CWZ lane closure strategies and traffic management plans. The pilot-car operation scenario results indicate that a one-lane closure with pilot-car operation on a two-lane road might consume 13% more fuel because of idling time and the slow movement of vehicles following the pilot car. This scenario generated emissions increases of 10% for CO2-e, 14% for NOx, 13% for SOx, and 65% for PM2.5.The results of these scenarios indicate that the impacts from heavy vehicles far exceed those from smaller vehicles in CWZs. Phase 2 of the study will develop methods for pavement management, conceptual evaluation, and project design that consider construction closures by implementing this life cycle assessment framework. These methods will also be used in studies to evaluate pavement design lives (20 years versus 40 years) and pavement selection for truck lanes and in-place recycling and to evaluate lane closure schedules and tactics to minimize CWZ impacts on highways by using project-specific traffic congestion levels.

Abstract Background In general, a slightly higher number of boys are born than girls, and the sex ratio at birth (number of male births/number of female births) is reported to be 1.03–1.07 in many countries. However, pregnant women exposed to high levels of atmospheric particulate matter have a reduced sex ratio at birth. Exposure to air pollutants can also lead to premature birth, suggesting that inflammation within the body may affect pregnancy maintenance and fetal development. On the other hand, the effects of air pollutants carried from afar by monsoons on the sex ratio at birth in downstream areas have not been evaluated. We focused on the Goto Islands, where various air pollutants flow from the Eurasian continent. The objective of this study was to clarify the effects of the atmospheric level of each pollutant on the sex ratio at birth on the Goto Islands. Methods We extracted observation data of particulate matter 2.5, sulfur dioxide, oxidants, nonmethane hydrocarbons, and methane from the National Institute for Environmental Studies database. In addition, the monthly sex ratio at birth was calculated using birth data from the National Statistics Center. To evaluate the effect of substance exposure just before fertilization on the sex ratio at birth, we analyzed the relationship between the observed pollutant level and the sex ratio at birth 9 months later. A stepwise generalized linear model was used to analyze the effects of air pollutant levels on the sex ratio at birth. Results The observed values for all pollutants were significantly different between seasons, including the particulate matter 2.5 (p

ObjectiveTo explore the association between air pollutants and hospital outpatient visits in a district of Shanghai.MethodsDaily meteorological data， environmental data， data of outpatient visits to two secondary hospitals and two tertiary hospitals in this district from January 1， 2015 to December 31， 2019 were collected. A Poisson regression generalized linear model was used to analyze the exposure-response relationship between the air pollutants and hospital outpatient visits in this area.ResultsDuring the study period， the total number of outpatient visits in the included hospitals was 17 802 634， with an average daily total of （9 750±4 191） outpatient visits，and an average daily of （761±341） respiratory outpatient visits. In the lag effect of single pollutant model， when the concentration of air pollutant increased by 10 μg·m-3， PM2.5， SO2， NO2 had the maximum lag effect on the number of outpatient visits in the department of internal medicine for respiratory diseases on lag day 4， day 5 and day 7， respectively. And the RR values and 95%CI were 1.002 0（1.001 3‒1.002 6）， 1.0154（1.012 3‒1.018 5）， and 1.006 1（1.005 3‒1.006 9）， respectively.ConclusionThere is a exposure-response relationship between air pollutants and the number of outpatient visits in each department of the hospitals， and different pollutants have different degrees of lag effects.

Abstract Background Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods. Methods Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016–2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children’s sex-specific associations were explored. Results After controlling for covariates, one standard deviation (SD) (11.5 μg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 – 26%) and 14% (95% CI: 1 – 29%) increase in SGA, respectively; and one SD (9.6 μg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 – 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22–32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found. Conclusion Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes.

Monitoring air pollution is important for human health and the environment. Previous studies on the prediction of air pollutants from satellite images have employed machine learning, yet there are few enhancements to the constructure of model. Moreover, the existing models have been successful in predicting pollutants like PM2.5, PM10, and O3. They have not been as effective in predicting other primary air pollutants. To improve the overall prediction performance of the existing model, a novel residual learning backpropagation model, abs. as BresNet, has been proposed in this research. This model has revealed the availability to precisely predict the ground-surface concentration of the six primary air pollutants, PM2.5, PM10, O3, NO2, CO, and SO2, based on the satellite imagery of MODIS AOD. Two of the most commonly used machine learning models so far, viz. the multilayer backpropagation neural network (MLBPN) and random forest (RF), were employed as the control. In the conducted experiments, the proposed BresNet model demonstrated significant improvements of 18.75%/31.94%, 33.82%/85.71%, 15.00%/35.29%, 39.06%/134.21%, 23.23%/68.00%, and 137.14%/260.87% in terms of R2 for the six primary air pollutants, compared to the RF/MLBPN model. Moreover, the convergence speed and loss function of the BresNet model compared to that of the MLBPN decreased by 55.15%, revealing superior convergence speed with the lower loss function.

Background: In general, a slightly higher number of boys are born than girls, and the sex ratio at birth (number of male births/number of female births) is reported to be 1.03–1.07 in many countries. However, pregnant women exposed to high levels of atmospheric particulate matter have a reduced sex ratio at birth. Exposure to air pollutants can also lead to premature birth, suggesting that inflammation within the body may affect pregnancy maintenance and fetal development. On the other hand, the effects of air pollutants carried from afar by monsoons on the sex ratio at birth in downstream areas have not been evaluated. We focused on the Goto Islands, where various air pollutants flow from the Eurasian continent. The objective of this study was to clarify the effects of the atmospheric level of each pollutant on the sex ratio at birth on the Goto Islands. Methods: We extracted observation data of particulate matter 2.5, sulfur dioxide, oxidants, nonmethane hydrocarbons, and methane from the National Institute for Environmental Studies database. In addition, the monthly sex ratio at birth was calculated using birth data from the National Statistics Center. To evaluate the effect of substance exposure just before fertilization on the sex ratio at birth, we analyzed the relationship between the observed pollutant level and the sex ratio at birth 9 months later. A stepwise generalized linear model was used to analyze the effects of air pollutant levels on the sex ratio at birth. Results: The observed values for all pollutants were significantly different between seasons, including the particulate matter 2.5 (p < 0.0001), sulfur dioxide (p = 0.0026), oxidant (p < 0.0001), nonmethane hydrocarbon (p < 0.0001), and methane (p < 0.0001) values. In the target population in the target period, the total number of births was 1835, and the sex ratio at birth was 0.967. Univariate analysis showed that the values of particulate matter 2.5 (p = 0.0157) and oxidants (p = 0.0047) correlated negatively with the sex ratio at birth. In addition, the results of multivariate analysis using the stepwise method in the model equation indicated that every 1 ppm increase in the observed OX value resulted in a 0.311 decrease in the sex ratio at birth (p = 0.0034). Conclusions: We evaluated the relationship between seasonal variations in air pollutant levels and the sex ratio at birth 9 months later on the Goto Islands. We found that an increase in oxidant levels just before and after conception may be a risk factor for a lower sex ratio at birth. Due to the previously reported vulnerability of male fetuses, females who become pregnant when air pollutant concentrations are high may be more likely to have a female baby. It is necessary to evaluate the effects of oxidants on various aspects of pregnancy and childbirth. [ABSTRACT FROM AUTHOR]

Ground level ozone is known to exhibit a strong daily variation of concentration leading to long-range transport of air pollutants from urban to rural areas. Moreover, the characteristics of O3 relationship between urban, suburban, and rural sites can be explained by O3 photochemical chemistry and meteorological dispersions as indicated by the different result of O3 diurnal pattern. However, little is known about the global phenomenon of diurnal concentration of ozone, meteorological dispersion such as longrange transport, and their correlation with ozone precursors, especially in urban and rural areas. This paper attempt to compare the difference between daily ozone fluctuations in both sites and assess some factors that cause longrange ozone transport from urban and rural areas both in subtropical and tropical areas for global scale. Using systematic literature review analysis with the PRISMA method, it examined 43 peer-reviewed articles published between 2010 and 2022 globally meeting the inclusion criteria. The result showed that the fluctuation patterns of daytime ozone in urban and rural areas are different to those in tropical and subtropical regions, depending on latitude. This was primarily due to the influence of solar radiation and the presence of precursors. Conversely, a slight decrease in ozone rate at night occurs because the precursor was accumulated by the shutdown of photochemical ozone production. Some precursors of ozone from other regions can be transported and accumulated from the long-range transport process in other locations. This paper serves as an initial guideline to analysing the pattern of ozone concentration in urban and rural areas and the factors that influence it. [ABSTRACT FROM AUTHOR]

Currently, Tangshan confronts the dual challenge of elevated carbon emissions and substantial pollution discharge from the iron and steel industries (ISIs). While significant efforts have been made to mitigate air pollutants and carbon emissions within the ISIs, there remains a gap in comprehending the control of carbon emissions, air pollutant emissions, and their contributions to air pollutant concentrations at the enterprise level. In this study, we devised the Air Pollutant and Carbon Emission and Air Quality (ACEA) model to identify enterprises with noteworthy air pollution and carbon emissions, as well as substantial contributions to air pollutant concentrations. We constructed a detailed inventory of air pollutants and CO2 emissions from the iron and steel industry in Tangshan for the year 2019. The findings reveal that in 2019, Tangshan emitted 5.75 × 104 t of SO2, 13.47 × 104 t of NOx, 3.55 × 104 t of PM10, 1.80 × 104 t of PM2.5, 5.79 × 106 t of CO and 219.62 Mt of CO2. The ACEA model effectively pinpointed key links between ISI enterprises emitting air pollutants and carbon dioxide, notably in pre-iron-making processes (coking, sintering, pelletizing) and the Blast furnace. By utilizing the developed air pollutant emission inventory, the CALPUFF model assessed the impact of ISI enterprises on air quality in the Tangshan region. Subsequently, we graded the performance of air pollutant and CO2 emissions following established criteria. The ACEA model successfully identified eight enterprises with significant air pollution and carbon emissions, exerting notable influence on air pollutant concentrations. Furthermore, the ACEA outcomes offer the potential for enhancing regional air quality in Tangshan and provide a scientific instrument for mitigating air pollutants and carbon emissions. The effective application of the ACEA model in Tangshan's steel industry holds promise for supporting carbon reduction initiatives and elevating environmental standards in other industrial cities across China. [ABSTRACT FROM AUTHOR]

The purpose of this study is to prepare the micro/nanorod materials in low middle temperature to remove the volatile organic compound (taking toluene as target) in the waste air pollution stream using a combined reactor with photo- and thermo-reaction at the same time. The micro/nanorod molecular sieve (Octahedral manganese cryptomelanes, K-OMS-2) are fabricated and coated on support of Al2O3, and the catalyst is used in the combined reactor with light and heat sources at feasible temperature to convert the target. The decomposition/removal efficiency of toluene in photo- and thermos-reactor as well as the photoelectric characteristic analyses are evaluated. The operation parameters of temperature of reactor and initial concentrations etc. are carried out, meanwhile, the characteristic analyses (EA, TEM-EDS, XRD, LaB6-TEM, XPS, FTIR etc.) are also examined in order to find the optimum conditions. In the performance of the photodegradation reaction, the optimum n is the catalyst of K-OMS-2/Al2O3 calcination temperature of 523 K with UVLED of 633 hr-1 space velocity (SV), and the maximum value is 95.56%. However, the CO2 conversion is not obvious in the photodegradation reaction due to the not complete degradation. In the photo- and thermosdegradation reactor, the situation can be improved and the maximum n of 95.25% and CO2a conversion of 95% can reach at 873K with UVLED. The result proves that the combined reactor for photo- and thermos-degradation can work in both industrial or domestic application in air pollution control. [ABSTRACT FROM AUTHOR]

Oil mist from polyester yarn and fabric production and metal cutting parts are important sources of air pollutants in urban atmospheres. Especially the 190–210 ℃ of flue gases emitted during the thermal fixation of fabrics containing spinning oil. To control the oil emissions, it is necessary to measure both the aerosol and gas phase oil vapor of the high-temperature flue gases discharged from stacks. A new method was developed to determine the oil content in the high-temperature flue gas stream by combining the measurement methods used separately for both aerosol and gas phases at room temperature in the literature with a hybrid approach. With this developed method, oil concentrations were determined in a total of 71 heat-fixing process stacks. The results showed that the oil mist concentration ranged from 0.23 to 297.89 mg/Nm3. It has been determined that the oil concentration was affected by factors such as fabric content, temperature, and whether there was a treatment unit. In the concentration distribution maps prepared in the Surfer program, oil vapor emissions, which were determined to be more intense in the regions close to the source, were revealed to be an important pollutant that should be controlled considering the effects on human health. [ABSTRACT FROM AUTHOR]

Dry eye disease (DED) is the most common disease affecting vision and quality of life. PM2.5 was a potential risk of DED. Herein, we conducted animal exposure and cell-based studies to evaluate the pathogenic effect of PM2.5 exposure on the ocular surface and DED etiological mechanisms. C57 mice were exposed to filtered air and PM2.5 aerosol. We assessed health conditions and inflammation of the ocular surface by corneal fluorescein staining and immunohistochemistry. In parallel, cultured human corneal epithelial cells (HCETs) were treated with PM2.5, followed by characterization of cell viability, intracellular ATP level, mitochondrial activities, and expression level of DED relevant mRNA and proteins. In mice, PM2.5 exposure induced severe superficial punctate keratopathy and inflammation in their cornea. In HCETs, cell proliferation and ROS generation followed dose-response and time-dependent manner; meanwhile, mitochondrial ROS (mtROS) level increased and mitochondrial membrane potential (MMP) level decreased. Inflammation cascade was triggered even after short-term exposure. The reduction of ATP production was alleviated with Nrf2 overexpression, NF-κB P65 knockdown, or ROS clearance. Nrf2 overexpression and P65 knockdown reduced inflammatory reaction through decreasing expression of P65 and increasing of Nrf2, respectively. They partly alleviated changes of ROS/mtROS/MMP. This research proved that PM2.5 would cause DED-related inflammation reaction on corneal epithelial cells and further explored its mechanism: ROS from mitochondrial dysfunctions of corneal epithelial cells after PM2.5 exposure partly inhibited the expression of anti-inflammatory protein Nrf2 led the activation of inflammatory protein P65 and its downstream molecules, which finally caused inflammation reaction. [ABSTRACT FROM AUTHOR]

The air quality is generally graded into five levels in a simple way or released by the Air Pollution Index (API) which does not take the fine particulate matter (PM2.5) emission into account. The fuzzy algorithm in this paper solves the mutability aroused by the grading approach by counting in the six items of pollutants pertaining to Short-lived climate forcers (SLCFs). Firstly, six items of air pollutants as well as driving factors of human activities are introduced. Secondly, based on the relevant observation data from the China Air Quality Online Monitoring Platform (CAQOMP), 18 experimental cities covering North China, East China, Central China, and South China are selected in this paper for air quality comparison and analysis in the application of the fuzzy algorithm. Thirdly, this paper looks into the air quality ranking of experimental 18 cities from a regional perspective. North China's air quality is not optimistic owing to being in a location near the coal producer and sand-sourced Outer Mongolia. Central China's air quality is more impacted by high-speed economic development and urbanization during the last couple of years. O3 emission is not well controlled across China. It turns out that the ranking by using the fuzzy algorithmic method is more scientific and holistic than API. [ABSTRACT FROM AUTHOR]

Background: Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods. Methods: Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016–2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children's sex-specific associations were explored. Results: After controlling for covariates, one standard deviation (SD) (11.5 μg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 – 26%) and 14% (95% CI: 1 – 29%) increase in SGA, respectively; and one SD (9.6 μg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 – 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22–32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found. Conclusion: Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes. [ABSTRACT FROM AUTHOR]

The first case of COVID-19 in Iran was reported on February 25, 2020, leading in the implementation of a government-mandated lockdown as the virus gradually spread to different cities. The objective of this study was to evaluate the impact of the COVID-19 pandemic on air quality in Ahvaz city by utilizing Sentinel 5 images and the Google Earth Engine (GEE) platform. Specifically, the concentrations of air pollutants, including CO, NO2, SO2, and HCHO, during the COVID-19 pandemic from May 10 to June 01, 2021, were examined. Also, they were compared to the same period in 2019. Additionally, the influence of meteorological parameters, such as wind speed and precipitation, on pollutant concentrations during the pandemic and in the pre-pandemic year of 2019 were investigated. The results revealed a significant decrease in the concentrations of NO2 (13.7%), CO (6.1%), SO2 (28%), and HCHO (9.5%) in Ahvaz during the study period in 2021 compared to the same period in 2019. Statistical analyses indicated no significant changes in wind speed and precipitation between the COVID-19 pandemic and the pre-pandemic period in 2019. Therefore, the impact of these parameters on the observed changes in pollutant concentrations can be disregarded. It is clear that the COVID-19 epidemic and the subsequent lockdown measures, including traffic restrictions and business closures, played a crucial role in significantly reducing air pollutant concentrations in Ahvaz. [ABSTRACT FROM AUTHOR]

Background: Constrained by no proper way to assess cumulative exposure, the joint effect of air pollution cumulative exposure doses on childhood asthma and wheezing (AW) was not understood. Objective: To assess the association between cumulative exposure to multiple air pollutants in early life and childhood AW. Methods: We designed a nested case-control study based on the birth cohort in Jinan City. Children with AW followed up within 2 years after birth were treated as cases, and non-cases in this cohort were treated as the control source population, and the propensity score matching method was used to match each case to 5 controls. We calculated the individual cumulative outdoor exposure doses for each period using an inverse distance weighted model, alongside the complex Simpson's formula, accounting for outdoor time and respiratory volume. The Least absolute shrinkage and selection operator (Lasso) regression was performed to screen for covariates. To analyze the joint effects of pollutants, we employed the weighted quantile sum (WQS) regression model in conjunction with conditional logistic regression. Results: 84 cases and 420 controls were included in this study. The odds ratio (OR) with 95% confidence interval (CI) of the impact of cumulative exposure (mg/m3) after birth on childhood AW was 1.78 (1.15–2.74) for SO2, 1.69 (1.11–2.57) for NO2, and 1.65 (1.09–2.52) for PM2.5, respectively. Furthermore, with each 25th percentile increase in the WQS index, the overall risk of cumulative doses for six pollutants exposure after birth on AW increased by an adjusted OR of 1.10 (1.03, 1.18), and SO2, PM2.5, and NO2 contributed the most to the WQS index. However, no statistically significant association was found between cumulative exposure to all pollutants before birth and childhood AW. Conclusions: There was a joint effect of the cumulative exposure dose of outdoor air pollutants after birth on AW in children aged 0–2 years. And traffic-related pollutants (SO2, PM2.5, and NO2) make a greater contribution to the joint effect.

Abstract Background Exposure to air pollutants promotes inflammation, cancer, and mortality in chronic diseases. Acute respiratory distress syndrome (ARDS) is a common condition among intensive care unit patients and is associated with a high mortality rate. ARDS is characterized by significant lung inflammation, which can be replicated in animal models by acute exposure to high doses of various air pollutants. Recently, several clinical studies have been conducted in different countries to investigate the role of chronic or acute air pollutant exposure in enhancing both ARDS incidence and severity. Results Chronic exposure studies have mainly been conducted in the US and France. The results of these studies suggest that some air pollutants, notably ozone, nitrogen dioxide, and particulate matter, increase susceptibility to ARDS and associated mortality. Furthermore, their impact may differ according to the cause of ARDS. A cohort study conducted in an urbanized zone in China showed that exposure to very high levels of air pollutants in the few days preceding intensive care unit admission was associated with an increased incidence of ARDS. The effects of acute exposure are more debatable regarding ARDS incidence and severity. Conclusion There is a likely relationship between air pollutant exposure and ARDS incidence and severity. However, further studies are required to determine which pollutants are the most involved and which patients are the most affected. Due to the prevalence of ARDS, air pollutant exposure may have a significant impact and could be a key public health issue.

This editorial discusses the impact of clinical and environmental toxicological exposures on eye health. It explores how factors such as weather, air pollution, chemicals, and medications can affect ocular health. The article includes studies that link environmental exposures to eye disorders such as dry eye disease, Sjögren's disease, and allergic conjunctivitis. It emphasizes the importance of understanding these associations and incorporating them into medical practice to mitigate risks for susceptible patients. Additionally, a specific study titled "Exposure-lag-response associations between extreme environmental conditions and primary Sjögren’s syndrome" investigates the potential links between extreme environmental conditions and primary Sjögren's syndrome, suggesting the need for further research in this area. [Extracted from the article]

This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM10, NO2, and PM2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population.

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.

Air quality monitoring is a significant job in our everyday life and affects the vitality of a human. It paves a better way of understanding the sources, effects and levels of pollutants in the air inhaled by the population of the region. This concept helps us in improving and developing pollution control measures to diminish the effect of air contamination. Air Quality Index (AQI) helps in understanding the level of pollution. This study briefs various state of art methods such as SVM, RF,ANN, RNN and FL in predicting air quality using machine learning. The results shows prominent outcomes which can be deployed in various cost effective hardware platforms for household and commercial purposes. The proposed model used eight parameters like NO2CO, O3, PM2.5, PM10, SO2, TEMP, PRES, DEWP, RAIN,WD, WSPM of Beijing dataset in order to predict AQI and it is tested with regional dataset. The Proposed model works based on process monitoring model. The efficient and flexible way to solve environmental issues is to combine machine learning algorithm with air quality prediction.

To achieve resilience goals, urban planners and decision-makers need accurate information on the benefits provided by urban trees and on the effects that management may have on them. This study investigates the impacts of management and disturbances on urban forest structure and function in Turku, Finland. Using a comprehensive urban tree database and the i-Tree software suite, we assessed the current structure and estimated the value of ecosystem services provided by Turku's urban forest. Additionally, we simulated changes in the urban forest over a 50-year period, considering different tree planting scenarios and the potential outbreak of the Asian longhorned beetle (ALB). Turku's urban forest comprised 38,438 public trees, dominated by Acer platanoides, Pinus sylvestris, Tilia × europaea, and Betula pendula. The estimated carbon storage was 12,336 t, valued at 1.98 million €, with an annual sequestration rate of 284 t (45,549 €/year). The trees also removed 8.97 t of pollutants annually, with an estimated value of 153,273 €. At the current rate of tree planting, the number of trees would decline over the course of 50 years resulting in a gradual decrease in the provision of ecosystem services. Although doubling the tree planting rate could slowly increase carbon storage and sequestration even under moderate ALB attack, it was insufficient to offset the damage caused by ALB if tree mortality rate reaches 50%. Compared to carbon storage and sequestration, changes in urban forest age structure had a more immediate impact on the removal of air pollution. These findings emphasize the importance of prioritizing investments in urban forests on grounds of their capacity to provide diverse ecosystem services. Incorporating these findings into decision-making processes would promote sustainable and resilient urban environments.

This article discusses different types of pollution from the paving process and the influence of using hot mix asphalt without and including end-of-life tyre rubber. This new composition type is one of the possible solutions to reduce the amount of used transport tyres and a new way to use less valuable raw materials. The research carried out involved analyzers applied under conditions of a mobile laboratory and handheld particle counters. Odour samples were taken using a vacuum chamber in Tedlar containers, and the level of odours was analyzed adopting the olfactometric method. Exposure to gaseous carbon, nitrogen and sulphur oxides and particulate matter up to 10 µm, fractions from 0.3 µm to 10 µm, as well as the odours emitted that cover the soil surface with the hot-mix asphalt-rubber mixture was examined. The average decrease in pollution using the asphalt-rubber mixture is about 3.8–4 % of nitrogen oxides, 10 % of particulate matter of 1.5–10, and an increase is approximately 9 % for particulate matter of 0.8–1 µm and 15 % for sulphur dioxide. The level of odour of compacted asphalt is around 2.8 times lower than that in the case of level asphalt.