Frequency and intensity of wildfire occurrences are dramatically increasing worldwide due to global climate change, having a devastating effect on the entire ecosystem including plants. Moreover, distribution of fire-smoke can influence the natural environment over very long distances, i.e. hundreds of kilometres. Dry plant matter contains 0.1–0.9% (w/w) sulphur, which is mainly released during combustion into the atmosphere as sulphur dioxide (SO₂) resulting in local concentrations of up to 3000 nL L⁻¹. SO₂ is a highly hazardous gas, which enters plants mostly via the stomata. Toxic sulphite is formed inside the leaves due to conversion of SO₂. Plants as sessile organisms cannot escape from threats, why they evolved an impressive diversity of molecular defence mechanisms. In the present study, two recent wildfires in Germany were evaluated to analyse the effect of SO₂ released into the atmosphere on deciduous trees: the Meppen peat fire in 2018 and the forest fire close to Luebtheen in 2019. Collected leaf material from beech (Fagus sylvatica) and oak (Quercus robur) was examined with respect to detoxification of sulphur surplus due to the exposure to elevated SO₂. An induced stress reaction in both species was indicated by a 1.5-fold increase in oxidized glutathione. In beech leaves, the enzymatic activities of the sulphite detoxification enzymes sulphite oxidase and apoplastic peroxidases were increased 5-fold and a trend of sulphate accumulation was observed. In contrast, oaks did not regulate these enzymes during smoke exposure, however, the constitutive activity is 10-fold and 3-fold higher than in beech. These results show for the first time sulphite detoxification strategies of trees in situ after natural smoke exposure. Beech and oak trees survived short-term SO₂ fumigation due to exclusion of toxic gases and different oxidative detoxification strategies. Beeches use efficient upregulation of oxidative sulphite detoxification enzymes, while oaks hold a constitutively high enzyme-pool available.

Stillbirth has a great impact on contemporary and future generations. Increasing evidence show that ambient air pollution exposure is associated with stillbirth. However, previous studies showed inconsistent findings. To clarify the effect of maternal air pollution exposure on stillbirth, we searched for studies examining the associations between air pollutants, including particulate matter (diameter ≤ 2.5 μm [PM₂.₅] and ≤10 μm [PM₁₀]) and gaseous pollutants (sulfur dioxide [SO₂], nitrogen dioxide [NO₂], carbon monoxide [CO] and ozone [O₃]), and stillbirth published in PubMed, Web of Science, Embase and Cochrane Library until December 11, 2020. The pooled effect estimates and 95% confidence intervals (CI) were calculated, and the heterogeneity was evaluated using Cochran’s Q test and I² statistic. Publication bias was assessed using funnel plots and Egger’s tests. Of 7546 records, 15 eligible studies were included in this review. Results of long-term exposure showed that maternal third trimester PM₂.₅ and CO exposure (per 10 μg/m³ increment) increased the odds of stillbirth, with estimated odds ratios (ORs) of 1.094 (95% CI: 1.008–1.180) and 1.0009 (95% CI: 1.0001–1.0017), respectively. Entire pregnancy exposure to PM₂.₅ was also associated with stillbirth (OR: 1.103, 95% CI: 1.074–1.131). A 10 μg/m³ increment in O₃ in the first trimester was associated with stillbirth, and the estimated OR was 1.028 (95% CI: 1.001–1.055). Short-term exposure (on lag day 4) to O₃ was also associated with stillbirth (OR: 1.002, 95% CI: 1.001–1.004). PM₁₀, SO₂ and NO₂ exposure had no significant effects on the incidence of stillbirth. Additional well-designed cohort studies and investigations regarding potential biological mechanisms are warranted to elaborate the suggestive association that may help improve intergenerational inequality.

Shandong is the most populous and highly industrialized province in eastern China, and the resultant poor air quality is a cause for widespread concern. This study combines bottom–up and top–down approaches to develop a high-resolution anthropogenic emission inventory of air pollutants for 2017. The inventory was developed based on updated emission factors and detailed activity data. The emissions of sulfur dioxide (SO₂), nitrogen oxides (NOₓ), particulate matter with aerodynamic diameters smaller than 2.5 and 10 μm (PM₂.₅ and PM₁₀, respectively), carbon monoxide (CO), volatile organic compounds (VOCs), and ammonia (NH₃) were estimated to be 1387.8, 2488.6, 5281.7, 3193.0, 9250.7, 2254.7, and 1210.6 kt, respectively. Power plants were the largest contributors of SO₂ and NOₓ emissions accounting for 43.7% and 41.9% of the total emissions, respectively. CO emissions mainly originated from industrial processes (40.1%), mobile sources (24.8%), and fossil fuel burning (21.2%). The major sources of PM₁₀ and PM₂.₅ emissions were industrial processes and fugitive dust, contributing 83.0% and 86.9% of their total emissions, respectively. Industrial processes (60.0%) contributed the largest VOC emissions, followed by mobile sources (16.8%) and solvent use (14.5%). Livestock and N-fertilizers were major emitters of NH₃, accounting for 69.9% and 21.2% of the total emissions, respectively. Emissions were spatially allocated to grid cells with a resolution of 0.05 ° × 0.05 ° based on spatial surrogates, using Geographic Information System (GIS). Heavy pollutant emissions were mainly concentrated in the central and eastern areas of Shandong, while high NH₃–emissions occurred in the western region. Most pollutant emissions from industrial sectors occurred in June and July, while low emissions were recorded between January and February. Range uncertainties in emission inventory were quantified using Monte Carlo simulations. Our inventory provides effective information to understand local pollutant emission characteristics, perform air quality simulations, and formulate pollution control measures.

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and particulate matter (PM₁₀). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO₂, SO₂, and PM₁₀ concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO₂ and SO₂ were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM₁₀ were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO₂ models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO₂ models were less robust with lower R² annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R² for PM₁₀ models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

To help understand the pathophysiologic mechanisms linking air pollutants and cardiovascular disease (CVD), we employed a repeated measures design to investigate the associations of four short-term air pollution exposures – particulate matter less than 2.5 μm in diameter (PM₂.₅), nitrogen dioxide (NO₂), ozone (O₃) and sulfur dioxide (SO₂), with two blood markers involved in vascular effects of oxidative stress, soluble lectin-like oxidized LDL receptor-1 (sLOX-1) and nitrite, using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Seven hundred and forty participants with plasma sLOX-1 and nitrite measurements at three exams between 2002 and 2007 were included. Daily PM₂.₅, NO₂, O₃ and SO₂ zero to seven days prior to blood draw were estimated from central monitors in six MESA regions, pre-adjusted using site-specific splines of meteorology and temporal trends, and an indicator for day of the week. Unconstrained distributed lag generalized estimating equations were used to estimate net effects over eight days with adjustment for sociodemographic and behavioral factors. The results showed that higher short-term concentrations of PM₂.₅, but not other pollutants, were associated with increased sLOX-1 analyzed both as a continuous outcome (percent change per interquartile increase: 16.36%, 95%CI: 0.1–35.26%) and dichotomized at the median (odds ratio per interquartile increase: 1.21, 95%CI: 1.01–1.44). The findings were not meaningfully changed after adjustment for additional covariates or in several sensitivity analyses. Pollutant concentrations were not associated with nitrite levels. This study extends earlier experimental findings of increased sLOX-1 levels following PM inhalation to a much larger population and at ambient concentrations. In light of its known mechanistic role in promoting vascular disease, sLOX-1 may be a suitable translational biomarker linking air pollutant exposures and cardiovascular outcomes.

Recent studies suggests that air pollution, from among others road traffic, can influence growth and development of the human foetus during pregnancy. The effects of air pollution from heavy industry on birth outcomes have been investigated scarcely.Our aim was to investigate the associations of air pollution from heavy industry on birth outcomes.A cross-sectional study was conducted among 4488 singleton live births (2012–2017) in the vicinity of a large industrial area in the Netherlands. Information from the birth registration was linked with a dispersion model to characterize annual individual-level exposure of pregnant mothers to air pollutants from industry in the area. Associations between particulate matter (PM₁₀), nitrogen oxides (NOX), sulphur dioxide (SO₂), and volatile organic compounds (VOC) with low birth weight (LBW), preterm birth (PTB), and small for gestational age (SGA) were investigated by logistic regression analysis and with gestational age, birth weight, birth length, and head circumference by linear regression analysis.Exposures to NOX, SO₂, and VOC (per interquartile range of 1.16, 0.42, and 0.97 μg/m³ respectively) during pregnancy were associated with LBW (OR 1.20, 95%CI 1.06–1.35, OR 1.20, 95%CI 1.00–1.43, and OR 1.21, 95%CI 1.08–1.35 respectively). NOX and VOC were also associated with PTB (OR 1.14, 95%CI 1.01–1.29 and OR 1.17, 95%CI 1.04–1.31 respectively). Associations between exposure to air pollution and birth weight, birth length, and head circumference were statistically significant. Higher exposure to PM₁₀, NOX, SO₂ and VOC (per interquartile range of 0.41, 1.16, 0.42, and 0.97 μg/m³ respectively) was associated with reduced birth weight of 21 g to 30 g.The 90th percentile industry-related PM₁₀ exposure corresponded with an average birth weight decrease of 74 g.

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in “mega” cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO₂ in Amman, Beirut, and Jeddah decreased by −56.6%, −43.4%, and −32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO₂ levels in megacities like Tehran (−0.9%) and Cairo (−3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

Air pollution could be a risk factor for the development of pterygium. This study aimed to investigate the potential associations between outpatient visits for pterygium and air pollutants. Using a time-stratified case-crossover design, the data of 3017 outpatients with pterygium visiting an eye center in Hangzhou, China, and the air pollution data of the Environmental Protection Department of Zhejiang Province between July 1, 2014, and November 30, 2019, were examined. The relationships between the air pollutants nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone, and fine particulate matter (PM) with median aerometric diameter <2.5 μm (PM₂.₅) and <10 μm (PM₁₀) and outpatient visits for primary pterygium were assessed using single- and multiple-pollutant models. Significant associations between outpatient visits for pterygium and air pollutants (PM₂.₅, PM₁₀, SO₂, and NO₂) were observed. Younger patients were found to be more sensitive to air pollution. Interestingly, the younger female patients with pterygium were more vulnerable to PM₂.₅ exposure during the warm season, while the younger male patients with pterygium were more sensitive to NO₂ during the cold season. Significant effects were also observed between the pterygium outpatients and PM₂.₅ (odds ratio [OR] = 1.06, P = 0.02), PM₁₀ (OR = 1.04, P = 0.01), and SO₂ (OR = 1.26, P = 0.01) during the warm season, as well as NO₂ (OR = 1.06, P = 0.01) during the cold season. Our study provides evidence that outpatient visits for pterygium are positively associated with increases in the air pollutants PM₂.₅, PM₁₀, SO₂, and NO₂, revealing the important role of air pollution in the occurrence and development of pterygium.

There is limited and equivocal epidemiological evidence relating to the association between maternal sulfur dioxide (SO₂) exposure and the risk of oral clefts (OCs) in offspring. We performed a population-based case-control study in Liaoning province to evaluate aforementioned relationship during 3 months before conception, the first trimester of pregnancy, and their single months. The study involved 3086 patients with OCs and 7950 controls. Data relating to SO₂ concentration was acquired from air monitoring stations throughout the study period. We used a multivariable logistic regression model to evaluate the association between exposure to SO₂ and the risk of OCs during the exposure windows. Maternal SO₂ exposure was positively related to OCs during the 3 months before conception (odds ratio = 1.38, 95% confidence interval: 1.15–1.65; P for trend < 0.01). Positive relationships were obtained from the first and second months before conception and the first month of pregnancy. Thus, our research reflects a relationship between SO₂ exposure and the risk of OCs. Future studies are now required to verify the association between SO₂ exposure and OCs during pregnancy and indicate the most relevant vulnerable exposure time windows.

Industrial sulfur dioxide (SO₂) has become an important source of environmental pollution in China, and the regional SO₂ emission reduction capacity is a comprehensive reflection of cleaner production capacity, environmental regulation, and economic development. It is obvious that high-tech industries play a crucial role in promoting the cleaner production capacity of the whole industry. Simultaneously, only considering the regional emission and the development of high-tech industry in isolation may deviate from actual economic characteristics. Therefore, by using the panel data of 30 provinces in China from 2005 to 2016, this paper adopts spatial autoregression model (SAR), spatial error model (SEM), and spatial Durbin model (SDM) to analyze the effect of the high-tech industry development on SO₂ emission reduction under the spatial adjacency matrix (W1), geographic distance matrix (W2), and economic distance matrix (W3). In addition, this paper selects three indicators, which is SO₂ removal rate, SO₂ emission, and SO₂ removal quantity, as explanatory variables, and R&D investment and number of enterprises in high-tech industry are selected to represent the industrial development level. The major conclusions are as follows: (1) The ability of SO₂ emission reduction in the local province is significantly affected by the surrounding provinces, showing the agglomeration characteristics of “high-high” or “low-low.” (2) The R&D investment of high-tech industry has a negative impact on SO₂ removal rate and SO₂ removal quantity, but a positive effect on the SO₂ emissions for the local province, and has a positive effect on the emission reduction of surrounding provinces. (3) The expansion of high-tech industry has significantly improved the SO₂ emission reduction capacity of the local province and its surrounding provinces. The robustness test supports the empirical conclusions of this paper. Finally, this paper puts forward some policy suggestions for government in environmental governance, such as “joint prevention and control” and the promotion of cleaner production.