Spatiotemporal variations of air pollution and population exposure in Shandong Province, eastern China, 2014-2018

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Independent and interaction effects of prenatal exposure to high AQI and extreme Humidex on the risk of preterm birth: A large sample population study in northern China

The combined effects of air pollution and extreme temperature on PTB remain unclear. To evaluate the independent effect and interaction effect of prenatal extreme exposure to air quality index (AQI) and Humidex, on PTB. Based on the National Health Care Data Platform of Shandong University, women who gave birth in 2019-2020 were selected for the study. First, the independent effects of AQI and Humidex on PTB were assessed by logistic regression model. Subsequently, the interaction effects of AQI and Humidex on PTB were estimated separately by calculation of the relative excess risk of interaction (RERI). A total of 34365 pregnant women were included and 1975 subjects were diagnosed with PTB. We observed a significant increase in the odds of PTB associated with maternal high AQI exposure, with an OR of 1.70 (95% CI: 1.59, 1.81). Similarly, extreme exposure to Humidex also demonstrated an elevated PTB odds, with a low Humidex OR of 2.48 (95% CI: 2.23, 2.76) and a high Humidex OR of 1.48 (95% CI: 1.31, 1.67). Finally, we observed an interaction between high AQI and extreme Humidex during the 1st trimester. Interaction effects were noted between high AQI and low Humidex throughout the entire trimester and the 2nd trimester. This study suggests that prenatal exposure to high AQI and extreme Humidex could increase the odds of PTB, with effects exhibiting the sensitivity window and a cumulative trend. Additionally, there is an interaction between AQI and Humidex.

MAX-DOAS and in-situ measurements of aerosols and trace gases over Dongying, China: Insight into ozone formation sensitivity based on secondary HCHO

This study presents a comprehensive overview of the atmospheric pollutants including Sulfur dioxide (SO2), Nitrogen dioxide (NO2), Formaldehyde (HCHO), Particulate Matter PM; PM10: diameter ≤ 10 µm, and PM2.5: diameter ≤ 2.5 µm), and Ozone (O3), over Dongying (Shandong Province) from March-April 2018 and September-October 2019 by employing ground-based Multiple Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations along with the in-situ measurements attained by the national air quality monitoring platform. The concentrations of SO2 and NO2 were under the acceptable level, while both PM2.5, and PM10 were higher than the safe levels as prescribed by national and international air quality standards. The results depict that 21% of the total observation days were found to be complex polluted days (PM2.5 > 35 µg/m3 and O3 > 160 µg/m3). The secondary HCHO was used for accurate analysis of O3 sensitivity. A difference of 11.40% and 10% during March-April 2018 and September-October 2019 respectively in O3 sensitivity was found between HCHOtotal/NO2 and HCHOsec/NO2. The results indicate that primary HCHO have significant contribution in HCHO. O3 formation predominantly remained to be in VOC-limited and transitional regime during March-April 2018 and September-October 2019 in Dongying. These results imply that concurrent control of both NOx and VOCs would benefit in ozone reductions. Additionally, the criteria pollutants (PM, SO2, and NO2) depicted strong correlations with each other except for O3 for which weak correlation coefficient was obtained with all the species. This study will prove to be baseline for designing of air pollution control strategies.

Air pollution trends and exceedances: ozone and particulate matter outlook in Brazilian highly urbanized zones

In Brazil, scarce air quality data hinders air pollutant chemical understanding and policy decisions regarding public health and environmental impacts. From this perspective, our study assessed the O3, PM2.5, and PM10 yearly and seasonal trends and also the WHO Air Quality Guidelines 2021 exceedance trends at 40 air quality stations located in four highly urbanized zones in Brazil (Belo Horizonte, São Paulo, Rio de Janeiro, and Espírito Santo) from early 1990s up to 2019. We applied the Mann-Kendall test aligned with Sen's Slope estimator to assess the trends and the Cox-Stuart test to verify the WHO AQG 2021 exceedances trends. Our findings pointed out that the current national legislation is outdated when compared to WHO AQG 2021 values, leading to multiple exceedances episodes. We also found out that 62% of São Paulo's stations presented O3 increasing trends, while in Rio de Janeiro 85.7% presented decreasing trends. The Cox-Stuart test pointed out that PM2.5 exceedance trends showcase positive values, and most of the significative values are located in São Paulo stations. Therefore, we endorse that the national legislation needs to be updated meanwhile the air monitoring network needs to expand its coverage.

County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region.