This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the causes of explosion during chemical mixing, drying, and pellet making. The Proposed ANN model predicts the accidents and the session of accidents (FN/AN) based on atmospheric conditions. This prediction takes values from historical accident data due to the atmospheric conditions of Sivakasi (2009–2021). In the development of ANN model, the Feed-Forward Back Propagation (FFBP) with the Levenberg-Marquardt function has been employed with hidden layers of 5 and 10 to train the network. The performance accuracy of both the hidden layers is evaluated and compared with other models like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN). The accuracy of the proposed model for accident classification is 82.7% and 67.8% for hidden layers 5 and 10, respectively. Also, the model predicts the session of accident with the accuracy of 72% and 54%, specificity of 77.7% and 60.1%, sensitivity of 69% and 52.92% for hidden layers 5 and 10, respectively. It is found that hidden layer 5 gives higher accuracy than hidden layer 10. The proposed ANN model gives the highest accuracy when compared to other models. This study is helpful in the firework industry management, and workers improve safety precautions and avoid explosions due to atmospheric conditions.

The increasing demand for fresh water coupled with the need to recycle water and nutrients has witnessed a global increase in wastewater irrigation. However, the development of antibiotic resistance hotspots in different environmental compartments, as a result of wastewater reuse is becoming a global health concern. The effect of irrigation water sources (wastewater, surface water, fresh water) on the presence and abundance of antibiotic resistance genes (ARGs) (blaCTX₋ₘ₋₃₂, tet-W, sul1, cml-A, and erm-B) and class 1 integrons (intI1) were investigated in the irrigation water-soil-crop continuum using quantitative real-time PCR (qPCR). Sul1 and blaCTX₋ₘ₋₃₂ were the most and least abundant ARGs in three environments, respectively. The abundance of ARGs and intI1 significantly decreased from wastewater to surface water and then fresh water. However, irrigation water sources had no significant effect on the abundance of ARGs and intI1 in soil and crop samples. Principal component analysis (PCA) showed that UV index and air temperature attenuate the abundance of ARGs and intI1 in crop samples whereas the air humidity and soil electrical conductivity (EC) promotes the ARGs and intI1. So that the climate condition of semi-arid regions significantly affects the abundance of ARGs and intI1 in crop samples. The results suggest that treated wastewater might be safely reused in agricultural practice in semi-arid regions without a significant increase of potential health risks associated with ARGs transfer to the food chain. However, further research is needed for understanding and managing ARGs transfer from the agricultural ecosystem to humans through the food chain.

With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O₃) pollution was generally amplified in China. However, the O₃ levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016–2019, while O₃ gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O₃ concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O₃ pollution mitigation during the lockdown. A 7.84 μg/m³ drop in O₃ concentration was driven by meteorology, with other decreases (4.11 μg/m³) explained by reduced anthropogenic emissions of O₃ precursors. Taken together, the lower NO₂/SO₂ ratios (1.25–2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O₃ concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24–58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O₃ reduction in the Guangxi region of South China.

Air and water quality at a concentrated animal feeding operation (CAFO) in Eastern North Carolina that uses a covered lagoon and anaerobic digester was evaluated for 2 weeks in August 2020. Real-time PM₂.₅ mass concentrations were determined using a reference ADR-1500 nephelometer and high-frequency measurements of dissolved inorganic nitrogen (DIN) were evaluated using autonomously logging sensors. Air and water quality parameters were assessed before, during and after wastewater from the lagoon was irrigated onto adjacent spray fields. Reference measurements were conducted alongside a HOBO weather station to collect real-time wind speed and direction, temperature, and humidity measurements. PM₂.₅ concentrations varied between 0 and 159 μg/m³ with an average concentration of 11 μg/m³, below EPA standard for secondary aerosols of 15 μg/m³. Higher PM₂.₅ concentrations were observed when wind originated from swine barns but not from covered lagoons. Water quality data showed that DIN concentrations downgradient from the CAFO were elevated relative to upstream concentrations. A groundwater seep that drains a spray field contained the highest average DIN concentration (31.0 ± 12.8 mg L⁻¹), which was 25 times greater than upstream DIN concentrations (1.2 ± 0.8 mg L⁻¹). Average DIN concentration at the downstream station was lower than the seep concentration (8.6 ± 16.2 mg L⁻¹), but approximately 8 times greater than upstream. Air quality data show that the lagoon cover was effective at mitigating air quality degradation, whereas DIN concentrations in water were similar to previous studies on CAFOs using open lagoons. In addition, air and water quality parameters were significantly (p < 0.001) higher after irrigation, indicating possible influence due to ammonia and nitrate elevation. Additional research is needed to compare high-frequency data collected from swine CAFOs using capped and uncapped lagoon systems to better understand spatiotemporal air and water quality trends of this practice.

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM₂.₅ (particles with an aerodynamic diameter smaller than 2.5 μm), PM₁₀ (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF_PSO was presented and the predictive performance was analyzed. The results show that the RF_PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM₂.₅, PM₁₀ and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques.

The present work describes spreading of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) at the tropical and temperate zones which are explained based on insolation energy, Particulate Matter (PM₂.₅), latitude, temperature, humidity, Population Density (PD), Human Development Index (HDI) and Global Health Security Index (GHSI) parameters. In order to analyze the spreading of SARS-CoV-2 by statistical data based on the confirmed positive cases which are collected between December 31, 2019 to April 25, 2020. The present analysis reveals that the outbreak of SARS-CoV-2 in the major countries lie on the Equator is 78,509 cases, the countries lie on the Tropic of Cancer is 62,930 cases (excluding China) and the countries lie on the Tropic of Capricorn is 22,842 cases. The tropical countries, which comes between the Tropic of Cancer and Tropic of Capricorn is reported to be 1,77,877 cases. The temperate zone countries, which are above and below the tropical countries are reported to be 25,66,171 cases so, the pandemic analysis describes the correlation between latitude, temperate zones, PM₂.₅ and local environmental factors. Hence, the temperature plays a pivotal role in the spreading of coronavirus at below 20 °C. The spreading of SARS-CoV-2 cases in Northern and Southern Hemispheres has inverse order against absorption of insolated energy. In temperate zone countries, the concentration of PM₂.₅ at below 20 μg/m³ has higher spreading rate of SARS-CoV-2 cases. The effect of insolation energy and PM₂.₅, it is confirmed that the spreading of SARS-CoV-2 is explained by dumb-bell model and solid/liquid interface formation mechanism. The present meta-analysis also focuses on the impact of GHSI, HDI, PD and PM₂.₅ on spreading of SARS-CoV-2 cases.

Recently, ground ozone has become one major airborne pollutant and the frequency of ozone-induced pollution episodes has increased rapidly across China. However, due to the lack of long-term observation data, relevant research on the characteristics and influencing factors of urban ozone concentrations remains limited. Based on ground ozone observation data during 2006–2016, we quantified the causality influence of individual meteorological factors on ozone concentrations in Beijing using a convergent cross mapping (CCM) method. The result indicated that the influence of each meteorological factor on ozone concentrations varied significantly across seasons and years. At the inter-annual scale, all-year meteorological influences on ozone concentrations were much more stable than seasonal meteorological influences. At the seasonal scale, meteorological influences on ozone concentrations were stronger in spring and autumn. Amongst multiple individual factors, temperature was the key meteorological influencing factor for ozone concentrations in all seasons except winter, when wind, humidity and SSD exerted major influences on ozone concentrations. In addition to temperature, air pressure was another meteorological factor that exerted strong influences on ozone concentrations. At both the inter-annual and seasonal scale, the influence of temperature and humidity on ozone concentrations was generally stable whilst that of other factors experienced large variations. Different from PM2.5, meteorological influences on ozone concentrations were relatively weak in summer, when ozone concentrations were the highest in Beijing. Given the generally stable meteorological influences on ozone concentrations and human-induced emissions of VOCs and NOx across seasons, warming induced notable increase in summertime biogenic emissions of VOCs and NOx can be a major driver for the increasing ozone pollution episodes. This research provides useful references for understanding long-term meteorological influences on ozone concentrations in mega cities in China.

The impact of ambient air pollution on pregnant women is a concern in China. However, little is known about the association between air pollution and preeclampsia and the potential modifying effects of meteorological conditions have not been assessed. This study aimed to assess the effects of prenatal exposure to air pollution on preeclampsia, and to explore whether temperature and humidity modify the effects. We performed a retrospective cohort study based on 1.21 million singleton births from the birth registration system in Shenzhen, China, between 2005 and 2012. Daily average measurements of particulate matter <10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), air temperature (T), and dew point (Td) were collected. Logistic regression models were performed to estimate associations between air pollution and preeclampsia during the first and second trimesters, and during the entire pregnancy. In each time window, we observed a positive gradient of increasing preeclampsia risk with increasing quartiles of PM10 and SO2 exposure. When stratified by T and Td in three categories (<5th, 5th −95th, and >95th percentile), we found a significant interaction between PM10 and Td on preeclampsia; the adverse effects of PM10 increased with Td. During the entire pregnancy, there was a null association between PM10 and preeclampsia under Td < 5th percentile. Preeclampsia risk increased by 23% (95% CI: 19–26%) when 5th < Td < 95th percentile, and by 34% (16–55%) when Td > 95th percentile. We also found that air pollution effects on preeclampsia in autumn/winter seasons were stronger than those in the spring/summer. This is the first study to address modifying effects of meteorological factors on the association between air pollution and preeclampsia. Findings indicate that prenatal exposure to PM10 and SO2 increase preeclampsia risk in Shenzhen, China, and the effects could be modified by humidity. Pregnant women should limit air pollution exposure, particularly during humid periods.

This study was designed to biomonitor the effect of PAH extracts from urban areas on the DNA of lung cell cultures. The analyses of the polycyclic aromatic hydrocarbons (PAHs) were performed in atmospheric PM₂.₅ and PM₁₀ collected at three sampling sites with heavy traffic located in the Metropolitan Area of Porto Alegre (MAPA) (Brazil). The concentrations of 16 major PAHs were determined according to EPA. Comet assay on V79 hamster lung cells was chosen for genotoxicity evaluation. Temperature, humidity, and wind speed were recorded. With regard to the damage index, higher levels were reported in the extract of particulate matter samples from the MAPA during the summer. High molecular weight compounds showed correlation with DNA damage frequency and their respective carcinogenicity.