Air pollution damages children’s health in many different ways, through both chronic and acute effects. The aims of our research are to reveal the indoor air quality levels in schools. Subject and indoor air measurements were performed in 34 primary schools located in the Central Anatolia region. PM10, PM2.5, CO2, CO, CH2O, relative humidity, temperature, and total bacteria and fungus levels were measured. In the urban region, mean PM1 was higher than the other regions(p=0.029). PM10 and PM2.5 were higher in schools in rural areas. According to CO2 measurements, only one school was identified to be below the upper limit recommended by the WHO. Total microorganism concentration was exceeded in 44.1% of classrooms. Indoor PM1, PM2.5, PM10, CO2, total bacteria and fungus levels were high and above recommended limits. Human activities, movements of students could be considered the most important indoor factors for particle matter increase. Indoor parameters could be lowered by organizing the school environment.

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.

Atmospheric models today represent all significant aerosol components. Atmospheric aerosols play an important role in the air, globally through their action on the Earth's radiation balance and locally through their effects on health in heavily polluted areas, they vary considerably in their properties that affect the way they absorb and disperse radiation, and they can thus have a cooling or warming effect, they impact on the formation and life of clouds is one example. Among the main sectors of activity releasing emissions of PM10 (fine particles with a diameter of less than 10 µm) and a diameter of less than 2.5 µm (PM2.5) is the industrial sector, in particular the extraction industry of building materials. The aerosols emitted by this type of industry are composed mainly of a mixture of dust, sulphates, carbon black and nitrates, is clearly perceptible in many continental regions of the northern hemisphere. Improvements in in situ, satellite and surface measurements are needed. However, the mechanisms by which aerosols interact with the environment are extremely complex and still poorly understood. This study is based on satellite atmospheric models to have spatiotemporal variability of concentrations of fine particles smaller than 10 µm in diameter (PM10) and smaller than 2.5 µm in diameter (PM2.5) at the level of the western Rif part of Morocco, home to a large number of extraction quarries and thus offering a high-resolution particle capture system (PM10 and PM2.5).

This study carries the evaluation of tolerance of sixteen plants against pollution. These plants have been selected and assessed for several phyto-socio-economic (tree height, canopy, type of tree, laminar structure, hardiness and economic value) and biochemical qualities (pH, Relative water content (R), Ascorbic acid (AA), Chlorophyll a (Chl a), (Chl b), Carotenoids (Car) and total Chlorophyll (TC)) and tested for Air pollution tolerance index (APTI) and Expected performance Index (EPI) and then EPI score used as grades of plants (Not Recommended, Very poor, Poor, Moderate, Good, Very good, Excellent and Best Plus Plant). Statistical analysis tool like correlation matrix among plant parameters and ANNOA test has been applied to understand the relationship among plant parameters and plant species. The best EPI score means best suited plant for the area for plantation and green belt development while plant with lower EPI may be used as bioindicators for the pollution because they are very sensitive for the air pollution.

A simple and rapid method for the highly sensitive determination of polycyclic aromatic hydrocarbons (PAHs) from airborne fine particulate matter (PM2.5) in an urban environment of Delhi was developed. The target compounds were 10 of the 16 United States Environmental Protection Agency (US-EPA) priority PAHs: fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo[a]pyrene, dibenzo(ah)anthracene, benzo(ghi)perylene, indeno(1,2,3-cd)pyrene. For collecting the samples, the following two locations in Delhi (India) were chosen: ITO and Okhla Industrial Area.  Two sets of samples at these locations of were collected for the purpose of investigation. The fine particulate matter samples were collected on glass fiber filter papers for 24h, from which the PAHs were extracted using dichloromethane (DCM) and hexane using ultrasonication method. Comparison of the characteristic emission of spectra of PAHs with standard spectra indicated the degree of condensation of aromatic compounds present in the investigated mixtures. However, this identification could be more effective with the use of the respective values of Δλ parameter for each particular component of the mixture. It has been found that the concentration of the PAHs is maximum during the winter season and minimum during the summer and monsoon seasons at both the locations.

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. | peer reviewed

Urbanization and Industrialization during the last few decades have increased air pollution causing harm to human health. Air pollution in metro cities turns out to be a serious environmental problem, especially in developing countries like India. The major environmental challenge is, to predict accurate air quality from pollutants. Envisaging air quality from pollutants like PM2.5, using the latest deep learning technique (LSTM timer series) has turned out to be a significant research area. The primary goal of this research paper is to forecast near-time pollution using the LSTM time series multivariate regression technique. The air quality data from Central Pollution Control Board over Hyderabad station has been used for the present study. All the processing is done in real-time and the system is found to be functionally very stable and works under all conditions. The Root Mean Square Error (RMSE) and R2 have been used as evaluation criteria for this regression technique. Further, the time series regression has been used to find the best fit model in terms of processing time to get the lowest error rate. The statistical model based on machine learning established a relevant prediction of PM2.5 concentrations from meteorological data.

To elucidate the molecular composition and sources of organic aerosols in Central Asia, carbonaceous compounds, major ions, and 15 organic molecular tracers of total suspended particulates (TSP) were analyzed from September 2018 to August 2019 in Dushanbe, Tajikistan. Extremely high TSP concentrations (annual mean ± std: 211 ± 131 μg m⁻³) were observed, particularly during summer (seasonal mean ± std: 333 ± 183 μg m⁻³). Organic carbon (OC: 11.9 ± 7.0 μg m⁻³) and elemental carbon (EC: 5.1 ± 2.2 μg m⁻³) exhibited distinct seasonal variations from TSP, with the highest values occurring in winter. A high concentration of Ca²⁺ was observed (11.9 ± 9.2 μg m⁻³), accounting for 50.8% of the total ions and reflecting the considerable influence of dust on aerosols. Among the measured organic molecular tracers, levoglucosan was the predominant compound (632 ± 770 ng m⁻³), and its concentration correlated significantly with OC and EC during the study period. These findings highlight biomass burning (BB) as an important contributor to the particulate air pollution in Dushanbe. High ratios of levoglucosan to mannosan, and syringic acid to vanillic acid suggest that mixed hardwood and herbaceous plants were the main burning materials in the area, with softwood being a minor one. According to the diagnostic tracer ratio, OC derived from BB constituted a large fraction of the primary OC (POC) in ambient aerosols, accounting for an annual mean of nearly 30% and reaching 63% in winter. The annual contribution of fungal spores to POC was 10%, with a maximum of 16% in spring. Measurements of plant debris, accounting for 3% of POC, divulged that these have the same variation as fungal spores.

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March–December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006–2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM₂.₅) and nitrogen dioxide (NO₂) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM₁₀ were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM₂.₅ and NO₂ was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

Considerable investigations have been carried out to address the relationship between ambient fine particulate matter (PM₂.₅) and blood pressure (BP) in patients with hypertension. However, few studies have explored the influence of PM₂.₅ and its constituents on Trimethylamine N-oxide (TMAO), an established risk factor for hypertension and cardiovascular disease (CVD), particularly in severely air-polluted areas. To explore the potential impact of PM₂.₅ constituents on BP, plasma hormones, and TMAO, a panel study was conducted to investigate changes in BP, plasma hormones, and TMAO in response to ambient air pollution exposure in stage 1 hypertensive young adults. Linear mixed effect models were used to estimate the cumulative effects of fine particulate matters (PM₂.₅) and its constituents on BP, plasma hormones and TMAO. We found that one interquartile range (IQR) (35 μg/m³) increase in 0–1 day moving-average PM₂.₅ concentrations was statistically significantly associated with elevated systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with estimated values of 0.13 (95% confidence interval (CI): 0.03 to 0.23) mmHg, 0.18 (95% CI: 0.08 to 0.28) mmHg, and 0.17 (95% CI: 0.09 to 0.26) mmHg, respectively. Hormone disturbance in the renin-angiotensin-aldosterone system was also associated with PM₂.₅ exposure. Elevated TMAO levels with an IQR increase for 0–4, 0–5, 0–6 moving-average concentrations of PM₂.₅ were found, and the increased values ranged from 26.28 (95% CI: 2.92 to 49.64) to 60.78 (31.95–89.61) ng/ml. More importantly, the PM₂.₅-bound metal constituents, such as manganese (Mn), titanium (Ti), and selenium (Se) showed robust associations with elevated BP and plasma TMAO levels. This study demonstrates associations between PM₂.₅ metal constituents and increased BP, changes in plasma hormones and TMAO, in stage 1 hypertensive young adults. Source control, aiming to reduce the emission of PM₂.₅-bound metals should be implemented to reduce the risk of hypertension and CVD.