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.

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily CO concentration as a function of 12 input variables. Then, forward selection (FS) technique was applied to reduce the number of input variables. After converting 12 input variables to 7 using the FS, they were fed to SVM models (FS-(-SVM) and FS-(-SVM)). Finally, a comparison among SVM models operation and previously developed techniques, i.e. classical regression model and artificial intelligent methods such as ANN and adaptive neuro-fuzzy inference system (ANFIS) was carried out. Determination of coefficient (R2) and mean absolute error (MAE) for -SVM (-SVM) were 0.87 (0.40) and 0.87 (0.41), respectively, while they were 0.90 (0.39) and 0.91 (0.35) for ANN and ANFIS, respectively. Results of developed SVM models indicated that both FS-(-SVM) and FS-(-SVM) regression techniques were superior. Furthermore, it was founded that the performance of FS-(-SVM) and FS-(-SVM) models were generally a bit better than the best FS-ANFIS and FS-ANN solutions for short term forecasting of CO concentrations.

Household air pollution (HAP) arising from combustion of biomass fuel (BMF) is a leading cause of morbidity and mortality in low-income countries. Air pollution may stimulate pro-inflammatory responses by activating diverse immune cells and cyto/chemokine expression, thereby contributing to diseases. We aimed to study cellular immune responses among women chronically exposed to HAP through use of BMF for domestic cooking. Among 200 healthy, non-smoking women in rural Bangladesh, we assessed exposure to HAP by measuring particulate matter 2.5 (PM₂.₅), black carbon (BC) and carbon monoxide (CO), through use of personal monitors RTI MicroPEM™ and Lascar CO logger respectively, for 48 h. Blood samples were collected following HAP exposure assessment and were analyzed for immunoprofiling by flow cytometry, plasma IgE by immunoassay analyzer and cyto/chemokine response from monocyte-derived-macrophages (MDM) and -dendritic cells (MDDC) by multiplex immunoassay. In multivariate linear regression model, a doubling of PM₂.₅ was associated with small increments in immature/early B cells (CD19⁺CD38⁺) and plasmablasts (CD19⁺CD38⁺CD27⁺). In contrast, a doubling of CO was associated with 1.20% reduction in CD19⁺ B lymphocytes (95% confidence interval (CI) = -2.36, −0.01). A doubling of PM₂.₅ and BC each was associated with 3.12% (95%CI = −5.85, −0.38) and 4.07% (95%CI = −7.96, −0.17) decrements in memory B cells (CD19⁺CD27⁺), respectively. Exposure to CO was associated with increased plasma IgE levels (beta(β) = 240.4, 95%CI = 3.06, 477.8). PM₂.₅ and CO exposure was associated with increased MDM production of CXCL10 (β = 12287, 95%CI = 1038, 23536) and CCL5 (β = 835.7, 95%CI = 95.5, 1576), respectively. Conversely, BC exposure was associated with reduction in MDDC-produced CCL5 (β = −3583, 95%CI = −6358, −807.8) and TNF-α (β = −15521, 95%CI = −28968, −2074). Our findings suggest that chronic HAP exposure through BMF use adversely affects proportions of B lymphocytes, particularly memory B cells, plasma IgE levels and functions of antigen presenting cells in rural women.

Air pollution is among the top risk faced by people around the world, and therefore combating it is among the top priorities. It begins with identifying the sources that contribute the most to local air pollution to prioritize their control. There are advanced methods for source identification and apportionment, but such methods are not available in many low-income countries and not everywhere in all high-income countries. We propose a simplified method by using source the signatures to help obtain information about the local source contribution if no other methods are available. Using low-cost monitors, particle mass (PM₂.₅) and carbon monoxide (CO) concentrations were measured and the ratio of CO/PM₂.₅ was determined. We investigated outdoor and indoor sources, including vehicular exhaust, combustion of biomass, incense and mosquito coil burning, and cigarette smoking. The results show that the ratios differed significantly between certain pollutant sources. Compressed natural gas (CNG) engines have a high ratio (mean value of 972 ± 419), which is attributed to relatively low PM₂.₅ emissions, while ship emissions and cigarette smoke recorded a relatively low ratio. Most traffic emissions recorded higher ratios than those of bushfire emissions, and ratios of most outdoor pollutant sources were much higher than those of indoor pollutant sources. There is a clear trend for ratios to decrease from high to low for CNG, petrol, diesel for buses, and fuel for ships. Our results suggest that the ratio of CO/PM₂.₅ can be used as an effective method to identify pollution sources.

This systematic review and meta-analysis evaluates the most recent evidence to examine whether use of improved biomass cookstoves in households in low-middle income countries results in reduction in mean concentrations of carbon monoxide (CO) and particulate matter of size 2.5 μm (PM₂.₅) in the cooking area, as well as reduction in mean systolic (SBP) and diastolic blood pressure (DBP) of adults using the cookstoves when compared to adults who use traditional three stone fire or traditional biomass cookstoves.We searched databases of scientific and grey literature. We included studies if published between January 2012 and June 2021, reported impact of ICS interventions in non-pregnant adults in low/middle-income countries, and reported post-intervention results along with baseline of traditional cookstoves. Outcomes included 24- or 48-h averages of kitchen area PM₂.₅, CO, mean SBP and DBP. Meta-analyses estimated weighted mean differences between baseline and post-intervention values for all outcome measures.Eleven studies were included; ten contributed estimates for HAP and four for BP. Interventions lead to significant reductions in PM₂.₅ (−0.73 mg/m³, 95% CI: −1.33, −0.13), CO (−8.37 ppm, 95%CI: −13.20, −3.54) and SBP (−2.82 mmHg, 95% CI: −5.53, −0.11); and a non-significant reduction in DBP (−0.80 mmHg, 95%CI: −2.33, 0.73), when compared to baseline of traditional cookstoves. Except for DBP, greatest reductions in all outcomes came from standard combustion ICS with a chimney, compared to ICS without a chimney and advanced combustion ICS.Among the reviewed biomass stove types, ICS with a chimney feature resulted in greatest reductions in HAP and BP.

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 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.

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.