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.

Effects of human activities on atmospheric nitrate (NO₃⁻) formation remain unclear, though the knowledge is critical for improving atmospheric chemistry models and nitrogen deposition reduction strategies. A potentially useful way to explore this is to compare NO₃⁻ oxidation processes in urban and rural atmospheres based upon the oxygen stable isotope composition of NO₃⁻ (Δ¹⁷O–NO₃⁻). Here we compared the Δ¹⁷O–NO₃⁻ from three-years of daily-based bulk deposition in urban (Shenyang) and forested rural sites (Qingyuan) in northeast China and quantified the relative contributions of different formation pathways based on the SIAR model. Our results showed that the Δ¹⁷O in Qiangyuan (26.2 ± 3.3‰) is significantly higher (p < 0.001) than in Shenyang (24.0 ± 4.0‰), and significantly higher in winter (Shenyang: 26.1 ± 6.7‰, Qingyuan: 29.6 ± 2.5‰) than in summer (Shenyang: 22.7 ± 2.9‰, Qingyuan: 23.8 ± 2.4‰) in both sites. The lower values in the urban site are linked with conditions that favored a higher relative contribution of nitrogen dioxide reaction with OH pathway (0.76-0.91) than in rural site (0.47-0.62), which should be induced by different levels of human activities in the two sites. The seasonal variations of Δ¹⁷O–NO₃⁻ in both sites are explained by a higher relative contribution of ozone-mediated oxidation chemistry and unfavorable conditions for the OH pathway during winter relative to summer, which is affected by human activities and seasonal meteorological condition change. Based on Δ¹⁷O, wintertime conditions led to a contribution of O₃ related pathways (NO₃ + DMS/HC and N₂O₅ hydrolysis) of 0.63 in Qingyuan and 0.42 in Shenyang, while summertime conditions led to 0.15 in Qingyuan and 0.05 in Shenyang. Our comparative study on Δ¹⁷O–NO₃⁻ between urban and rural sites reveals different anthropogenic effects on nitrate formation processes on spatial and temporal scales, illustrating different responses of reactive nitrogen chemistry to changes in human activities.

Construction workers on highway rehabilitation projects can be exposed to a combination of traffic- and construction-related emissions. To assess the personal exposure a worker experiences, a portable battery-operated Air Quality Device (AQD) was utilised to measure emissions during normal construction operations of a major road rehabilitation project. Emissions measured were nitrogen dioxide (NO₂), Total Volatile Organic Compounds (TVOCs) and Particulate Matter (PM₁₀, PM₂.₅, and PM₁). The objective of the paper is to document the hazardous emissions that construction workers may be exposed to and allow for a basis of informed decision making to mitigate the risks of a road construction project. Most critically, this article is designed to raise awareness of the potential impact to a worker's wellbeing as well as highlight the need for further research. Through statistical analysis, asphalt paving was identified as the most hazardous activity in terms of exposure relative to other activities. This activity was further assessed using discrete-time Markov chain Monte Carlo simulations with results indicating a high probability that workers may be exposed to greater hazardous emission concentrations than measured. Limiting the distance to the source of emissions, large-scale use of warm-mix asphalt and reducing the idling times of construction vehicles were identified as practical mitigation measures to reduce exposure and aid in achieving zero-harm objectives. Finally, it is found that males are more susceptible to long-term implications of hazardous emission inhalation and should be more aware if the scenarios they might work in expose them to this.

Responses to COVID-19 altered environmental exposures and health behaviours associated with non-communicable diseases. We aimed to (1) quantify changes in nitrogen dioxide (NO₂), noise, physical activity, and greenspace visits associated with COVID-19 policies in the spring of 2020 in Barcelona (Spain), Vienna (Austria), and Stockholm (Sweden), and (2) estimated the number of additional and prevented diagnoses of myocardial infarction (MI), stroke, depression, and anxiety based on these changes. We calculated differences in NO₂, noise, physical activity, and greenspace visits between pre-pandemic (baseline) and pandemic (counterfactual) levels. With two counterfactual scenarios, we distinguished between Acute Period (March 15th – April 26th, 2020) and Deconfinement Period (May 2nd – June 30th, 2020) assuming counterfactual scenarios were extended for 12 months. Relative risks for each exposure difference were estimated with exposure-risk functions. In the Acute Period, reductions in NO₂ (range of change from −16.9 μg/m³ to −1.1 μg/m³), noise (from −5 dB(A) to −2 dB(A)), physical activity (from −659 MET*min/wk to −183 MET*min/wk) and greenspace visits (from −20.2 h/m to 1.1 h/m) were largest in Barcelona and smallest in Stockholm. In the Deconfinement Period, NO₂ (from −13.9 μg/m³ to −3.1 μg/m³), noise (from −3 dB(A) to −1 dB(A)), and physical activity levels (from −524 MET*min/wk to −83 MET*min/wk) remained below pre-pandemic levels in all cities. Greatest impacts were caused by physical activity reductions. If physical activity levels in Barcelona remained at Acute Period levels, increases in annual diagnoses for MI (mean: 572 (95% CI: 224, 943)), stroke (585 (6, 1156)), depression (7903 (5202, 10,936)), and anxiety (16,677 (926, 27,002)) would be anticipated. To decrease cardiovascular and mental health impacts, reductions in NO₂ and noise from the first COVID-19 surge should be sustained, but without reducing physical activity. Focusing on cities’ connectivity that promotes active transportation and reduces motor vehicle use assists in achieving this goal.

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM₂.₅), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53–0.87 (PM₂.₅), 0.53–0.85 (PM₁₀), and 0.33–0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM₂.₅ and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM₂.₅/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM₂.₅ and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Children’s respiratory health are particularly vulnerable to outdoor air pollution, but evidence is lacking on the very acute effects of air pollution on the risk of acute upper respiratory infections (AURI) and acute lower respiratory infections (ALRI) in children. This study aimed to evaluate the risk of cause-specific AURI and ALRI, in children within 24 h of exposure to air pollution. We obtained data on emergency cases, including 11,091 AURI cases (acute pharyngitis, acute tonsillitis, acute obstructive laryngitis and epiglottitis, and unspecified acute upper respiratory infections) and 11,401 ALRI cases (pneumonia, acute bronchitis, acute bronchiolitis, unspecified acute lower respiratory infection) in Brisbane, Australia, 2013–2015. A time-stratified case-crossover analysis was used to examine the hourly association of AURI and ALRI with high concentration (95th percentile) of four air pollutants (particulate matters with aerodynamic diameter <10 μm (PM₁₀) and <2.5 μm (PM₂.₅), ozone (O₃), nitrogen dioxide (NO₂)). We observed increased risk of acute tonsillitis associated with PM₂.₅ within 13–24 h (odds ratio (OR), 1.45; 95% confidence interval [CI], 1.02–2.06) and increased risk of unspecified acute upper respiratory infections related to O₃ within 2–6 h (OR, 1.38, 95%CI, 1.12–1.70), NO₂ within 1 h (OR, 1.19; 95%CI, 1.01–1.40), and PM₂.₅ within 7–12 h (OR, 1.21; 95%CI, 1.02–1.43). Cold season and nigh-time air pollution has greater effects on AURI, whereas greater risk of ALRI was seen in warm season and daytime. Our findings suggest exposures to particulate and gaseous air pollution may transiently increase risk of AURI and ALRI in children within 24 h. Prevention measures aimed at protecting children’s respiratory health should consider the very acute effects of air pollution.

Air pollution is an important risk factor for autoimmune diseases, but its association with the recurrence of rheumatoid arthritis (RA) remains unclear so far. This study aimed to investigate the short-term association between traffic-related air pollutants and hospital readmissions for RA in Hefei, China. Data on daily hospital readmissions for RA and traffic-related air pollutants, including particulate matter (PM₂.₅ and PM₁₀), nitrogen dioxide (NO₂), and carbon monoxide (CO), from 2014 to 2018 were retrieved. A time-series approach using generalized linear regression model was employed. The analysis was further stratified by sex, age and season. A total of 1153 readmissions for RA were reported during the study period. A significant association between high-concentration PM₂.₅ (90th percentile) and RA readmissions was observed on lag1 (relative risk (RR) = 1.09, 95% confidence interval (CI): 1.01–1.19) and lasted until lag3 (RR = 1.06, 95%CI: 1.01–1.12). From lag2 to lag5, high-concentration NO₂ (90th percentile) was associated with increased risk of RA readmissions, with the highest RR observed at lag 4 (1.11, 95%CI: 1.05–1.17). Stratified analyses indicated that females and the elderly appeared to be more vulnerable to high-concentration PM₂.₅ and NO₂ exposure. High-concentration PM₂.₅ and NO₂ in cold seasons were consistently significantly associated with increased risk of RA readmissions. Exposure to high-concentration PM₂.₅ and NO₂ was associated with increased risk of RA readmissions. Protective measures against the exposure to high-concentration PM₂.₅ and NO₂ should be taken to reduce the recurrence risk in RA patients, especially in females, the elderly and during cold seasons.

During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO₂ (nitrogen dioxide), PM₁₀ (particulate matter), O₃ (ozone) and Oₓ (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city’s lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. The analysis indicated that the city’s average concentration reductions for the lockdown period were: -36.9 to −41.6%, and −6.6 to −14.2% for NO₂ and PM₁₀, respectively. However, an increase of 11.6–33.8% for O₃ was estimated. The reduction in pollutant concentration, especially NO₂ can be explained by significant drops in traffic-flows during the lockdown period (−51.6 to −43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.