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.

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.

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.

Exposure to household air pollution (HAP) from solid fuel use (SFU) causes millions of premature deaths globally. Direct leakage from stoves into indoor air is believed to be the main cause of severe HAP. However, previous laboratory-based measurements reported leakage of minimal fractions from wood fuel combustion. Using a newly developed measurement method, on-site measurements were conducted to quantitatively evaluate the leakage of gases and particulate matter from different fuel-stove combinations. The fraction of indoor leakage to the total emission (F) of the measured air pollutants varied from 23 ± 11% to 40 ± 16% for different pollutants and fuel-stove combinations, and these were significantly higher than previously lab-based results. Fuel differences overwhelmed stove differences in influencing F values, with higher values from biomass burning than from coal combustion. The particles had higher F values than gases. Fugitive emission rates (ERs) were log-normally distributed, and biomass burning had higher ERs than coal burning. Indoor PM₂.₅ (fine particulate matter) and CO (carbon monoxide) concentrations measured during the burning period increased by nearly 1–2 orders of magnitude compared to concentrations before or after burning, confirming substantially high indoor leakage from fuel combustion in cookstoves. High fugitive emissions in indoor cookstoves quantified from the present on-site measurements effectively explain the high HAP levels observed in rural SFU households, and call for interventions to improve indoor air quality.

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.

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.

Ambient carbon monoxide (CO) and particulate matters (PMs) are two important air pollutants in urban areas with known impacts on fetuses. Hence, this study measured some biochemistry factors of 200 neonates with birth dates from January 19 to October 12, 2020, including the birth weight and height and the serum levels of ALT, AST, ALP, GGT, and TSH. The Support Vector Machine-fitted land-use regression approach was used to predict the spatio-temporal variability of intra-urban PM 2.5 and CO concentrations by month during the pregnancy period of the cases employing 5 variables of Digital Elevation Model (DEM), slope, and distance from Compressed Natural Gas (CNG) stations, Bus Rapid Transit (BRT) stations, and mines and industries. Spearman correlation analysis (p < 0.05) was performed between the neonate indices and mean monthly PM 2.5 and CO concentrations at the exact residential address of maternal cases and their nearby areas in 250, 500, 1000, 1500, and 2000 m-radius buffer rings. All modeling efforts succeeded in predicting CO and PM 2.5 levels with acceptable adjusted r² values. Northern Isfahan had relatively higher CO and PM 2.5 concentrations due to its adjacency to low-vegetated open lands and its high traffic load as compared to southern areas. The correlation results between the neonate biochemistry indices and mean PM 2.5 and CO concentrations were mostly positive in most buffer rings, especially in the >500 m-radius buffer rings for PM 2.5 and in the 2000 m-radius rings for CO. Although the correlation results of PM 2.5 followed a detectable trend in the buffer rings, the associations between CO and the neonate biochemistry indices differed significantly between the buffer rings. Results showed that increasing mean monthly concentration of CO and PM 2.5 may stimulate further production of liver enzymes while decreasing the birth weight and height.

Commuters are exposed to a variety of physicochemical and microbiological pollutants that can lead to adverse health effects. This study aims to evaluate the indoor air quality (IAQ) in cars, buses and trains in Lisbon, to estimate inhaled doses while commuting and to evaluate the impacts of cleaning and ventilation on the IAQ. Particulate matter with diameter lower than 1, 2.5 and 10 μm (PM₁, PM₂.₅ and PM₁₀), black carbon (BC), carbon monoxide (CO), carbon dioxide (CO₂) volatile organic compounds (VOCs), formaldehyde (CH₂O) and total airborne bacteria and fungi were measured and bacterial isolates were identified. Results showed that the type of ventilation is the main factor affecting the IAQ in vehicle cabins. Under the fan off condition, the concentration of BC was lower, but the concentration of gases such as CO₂, CO and VOC tended to accumulate rapidly. When the ventilation was used, the coarse particles were filtered originating the decrease of indoor concentrations. Commuters travelling in trains received the lowest dose for all chemical pollutants, except VOC, mainly because railways are further away from the direct vehicular emissions. Commuters travelling in cars without ventilation received the highest inhaled dose for almost all pollutants despite having the lowest travel duration. Airborne microbiota was highly affected by the occupancy of the vehicles and therefore, the fungi and bacterial loads were higher in trains and buses. Most of the isolated species were human associated bacteria and some of the most abundant species have been linked to respiratory tract infections.