For the first time, sulfur dioxide concentration was monitored between 2005 and 2016 over Iran which is among the countries with a high SO2 emission rate in the world. To that end, SO2 column concentration at Planetary Boundary Layer (PBL) from Ozone Monitoring Instrument (OMI) was analyzed. OMI is a sensor onboard the Aura satellite which can measure daily SO2 concentration on the global scale. From OMI maps, 19 notable SO2 hotspots were detected over Iran. The results indicate that the most elevated level of SO2 among these 19 hotspots belong to Khark Island and Asaluye in Bushehr province, southwest of Iran. Annual trend analysis shows that SO2 concentration has been slightly augmented during 2005-2016 over this country. Distribution analysis of SO2 concentration over Iran showed that the most polluted provinces are Bushehr, Khuzestan and Ilam lied in the southwest of Iran. On the contrary, the lowest level of SO2 has observed over northwest of Iran at West and East Azerbaijan and Ardabil provinces. The correlation coefficient between total energy production in Iran and SO2 concentration from 2005 to 2016 is as high as ~0.7. Hence, it can be derived that energy production, most notably production of crude oil, plays a pivotal role in SO2 concentration over Iran.

In recent times, the brick kiln contributes to air pollution is one of the most emerging issues worldwide. In this research work, the Peshawar city, ambient air quality was measured, using a fixed air monitoring station to evaluate the impact of gaseous emission from brick kilns on ground level. In this study, the portable gas analyzer (PG-250) was used to quantify brick-based emitting carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen oxide (NOx) from 3 brick kilns in the city of Peshawar. It was noticed that the average concentration of SO2 and NOx exceeds the National Environmental Quality Standards (NEQS) of Pakistan specifically, in terms of air quality. The brick kilns in District Peshawar have shown negative effects on the environment. It is necessary to take various measures to monitor the brick kiln embosom regularly before it becomes a significant risk for individuals. In conclusion, the impact of air pollution on physical activity and sedentary behavior at a specific time may be different.

The present research analyzes air quality in Ahvaz, a city in the south of Iran, paying special attention to sulfur dioxide (SO2). In order to prepare the average data in the city, measurements have been carried out between 2009 and 2010 in two different locations. Relations between sulfur dioxide and some meteorological parameters have been calculated statistically, using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation and rainfall have been considered as independent variables. The RMSE Test showed that among different prediction models, the stepwise one is the best option. The average concentrations have been calculated for every 24 hours, during each month and each season. Results show that the highest concentration of sulfur dioxide occurs generally in the morning while the lowest concentration is found before the sunshine. In case of the monthly concentrations of sulfur dioxide, the highest value belongs to January, while the lowest one occurs in October. And as for the seasonal concentrations, it has been shown that the highest amounts belong to winter. Results show that quantities of SO2 in different seasons as well as the entire year can be estimated by climate parameters. Results also indicate that the relations between the SO2 and meteorological parameters are stronger than the entire year during the seasons.

peer reviewed | In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed.

Studies have linked gaseous air pollutants to multiple health effects via inflammatory pathways. Several major inflammatory biomarkers, including C-reactive protein (CRP), fibrinogen, interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) have also been considered as predictors of cardiovascular disease. However, there has been no meta-analysis to evaluate the associations between gaseous air pollutants and these typical biomarkers of inflammation to date. To evaluate the overall associations between short-term and long-term exposures to ambient ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon dioxide (CO) and major inflammatory biomarkers including CRP, fibrinogen, IL-6 and TNF-α. A meta-analysis was conducted for publications from PubMed, Web of Science, Scopus and EMBASE databases up to Feb 1st, 2021. The meta-analysis included 38 studies conducted among 210,438 participants. Generally, we only observed significant positive associations between short-term exposures to gaseous air pollutants and inflammatory biomarkers. For a 10 μg/m³ increase in short-term exposure to O₃, NO₂, and SO₂, there were significant increases of 1.05% (95%CI: 0.09%, 2.02%), 1.60% (95%CI: 0.49%, 2.72%), and 10.44% (95%CI: 4.20%, 17.05%) in CRP, respectively. Meanwhile, a 10 μg/m³ increase in NO₂ was also associated with a 4.85% (95%CI: 1.10%, 8.73%) increase in TNF-α. Long-term exposures to gaseous air pollutants were not statistically associated with these biomarkers, but the study numbers were relatively small. Subgroup analyses found more apparent associations in studies with better study design, higher quality, and smaller sample size. Meanwhile, the associations also varied across studies conducted in different geographical regions. Short-term exposure to gaseous air pollutants is associated with increased levels of circulating inflammatory biomarkers, suggesting that a systemic inflammatory state is activated upon exposure. More studies on long-term exposure to gaseous air pollutants and inflammatory biomarkers are warranted to verify the associations.

We provide a comprehensive and updated systematic review and meta-analysis of the association between air pollution exposure and depression, searching PubMed, Embase, and Web of Sciences for relevant articles published up to May 2021, and eventually including 39 studies. Meta-analyses were performed separately according to pollutant type [particulate matter with diameter ≤10 μm (PM₁₀) and ≤2.5 μm (PM₂.₅), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO)] and exposure duration [short- (<30 days) and long-term (≥30 days)]. Test for homogeneity based on Cochran's Q and I² statistics were calculated and the restricted maximum likelihood (REML) random effect model was applied. We assessed overall quality of pooled estimates, influence of single studies on the meta-analytic estimates, sources of between-study heterogeneity, and publication bias. We observed an increased risk of depression associated with long-term exposure to PM₂.₅ (relative risk: 1.074, 95% confidence interval: 1.021–1.129) and NO₂ (1.037, 1.011–1.064), and with short-term exposure to PM₁₀ (1.009, 1.006–1.012), PM₂.₅ (1.009, 1.007–1.011), NO₂ (1.022, 1.012–1.033), SO₂ (1.024, 1.010–1.037), O₃ (1.011, 0.997–1.026), and CO (1.062, 1.020–1.105). The publication bias affecting half of the investigated associations and the high heterogeneity characterizing most of the meta-analytic estimates partly prevent to draw very firm conclusions. On the other hand, the coherence of all the estimates after excluding single studies in the sensitivity analysis supports the soundness of our results. This especially applies to the association between PM₂.₅ and depression, strengthened by the absence of heterogeneity and of relevant publication bias in both long- and short-term exposure studies. Should further investigations be designed, they should involve large sample sizes, well-defined diagnostic criteria for depression, and thorough control of potential confounding factors. Finally, studies dedicated to the comprehension of the mechanisms underlying the association between air pollution and depression remain necessary.

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.

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.

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.