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.

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.

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.

Reduction of particulate matter (PM) has emerged as one of the most significant challenges in public health and environment protection worldwide. To address PM-related problems and effectively remove fine particulate matter (PM2.5), environmentalists proposed tree planting and afforestation as eco-friendly strategies. However, the PM removal effect of plants and its primary mechanism remains uncertain. In this study, we experimentally investigated the PM removal performance of five plant species in a closed chamber and the effects of relative humidity (RH) caused by plant evapotranspiration, as a governing parameter. On the basis of the PM removal test for various plant species, we selected Epipremnum aureum (Scindapsus) as a representative plant to identify the PM removal efficiency depending on evapotranspiration and particle type. Results showed that Scindapsus yielded a high PM removal efficiency for smoke type PM2.5 under active transpiration. We examined the correlation of PM removal and relative humidity (RH) and evaluated the increased effect of RH on PM2.5 removal by using a plant-inspired in vitro model. Based on the present results, the increase of RH due to evapotranspiration is crucial to the reduction of PM2.5 using plants.

Mercury (Hg) is a pervasive environmental contaminant that can impair avian health, consequently there is a need to gauge exposures. Bird blood provides a measure of recent dietary exposure to Hg, but blood collection and storage can be complex and costly. Dried blood spots (DBS) may help overcome challenges of whole blood analyses, therefore, this study aimed to develop and validate a novel method to assess Hg exposure in birds using DBS. First, accuracy and precision of blood Hg concentrations for entire DBS and DBS punches were determined for white leghorn chicken (Gallus gallus domesticus) dosed with methylmercury (MeHg) via egg injection. Next, we investigated Hg stability in chicken DBS subjected to time, temperature, and humidity treatments. Lastly, we applied the method to DBS created using standard field methods from zebra finch (Taeniopygia guttatato) in the laboratory and American golden-plover (Pluvalis dominica) sampled in the field. All samples were analyzed for total Hg (THg) using direct Hg analysis. Accuracy was determined by comparing DBS concentrations with those of corresponding whole blood and reported as percent recovery. Accuracy for entire chicken DBS was 101.8 ± 5.4%, while DBS punches revealed lower recovery (87.7 ± 4.0 to 92.4 ± 4.1%). There was little effect of time, temperature, and humidity storage treatments on Hg concentrations of DBS, with mean DBS THg concentrations within ±8% of whole blood (n = 10 treatments). For zebra finch, DBS punches were more accurate (93.7 ± 9.7%) compared to entire DBS (126.8 ± 19.4%). While for American golden-plover, entire DBS resulted in the most accurate THg concentrations (111.5 ± 7.6%) compared to DBS punches (edge: 115.4 ± 18.9%, interior: 131.4 ± 16.1%). Overall, results indicate that DBS analysis using direct Hg analysis can accurately evaluate Hg exposure in birds.

The prevalence of allergic diseases has increased in the past few decades. Bio-aerosol proteins and their chemical modifications, such as 3-nitrotyrosine (3-NT), in the atmosphere have been attracting attention due to their promotive effects on allergies. 3-NT is generated from the amino acid, tyrosine, through a reaction with ozone (O3) and nitrogen dioxide (NO2). However, the underlying mechanisms have not yet been elucidated in detail. Therefore, we measured 3-NT and evaluated the relationships among 3-NT and various pollutants such as sulfur dioxide (SO2), NOx (NO + NO2), ozone (O3), PM7, total suspended particulate matter (TSP) containing proteins, humidity, and temperature. 3-NT positively correlated with O3, SO2, humidity, and temperature, and negatively correlated with NOx. A multiple regression analysis showed that 3-NT positively associated with O3, humidity, and PM7. O3 positively associated with 3-NT and PM7, and negatively associated with NOx and humidity. These results suggest that 3-NT is generated from PM proteins through a reaction with O3 under high humidity conditions, and that the measurement of 3-NT is important and useful for the research of O3.

PM2.5 concentrations in a typical residential apartment in Beijing and immediately outside of the building were measured simultaneously during heating and non-heating periods. The objective was to quantitatively explore the relationship between indoor and outdoor PM2.5 concentrations. A statistical method for predicting indoor PM2.5 concentrations was proposed. Ambient PM2.5 concentrations were strongly affected by meteorological conditions, especially wind directions. A bimodal distribution was identified during the heating season due to the frequent and rapid transition between severe pollution events and clean days. Indoor PM2.5 concentrations were significantly correlated with outdoor PM2.5 concentrations but with 1–2 h delay, and the differences can be explained by ambient meteorological features, such as temperature, humidity, and wind direction. These results indicate the potential to incorporate indoor exposure features to the regional air quality model framework and to more accurately estimate the epidemiological relationship between human mortality and air pollution exposure.

Proteinaceous matter is an important component of PM₂.₅, which can cause adverse health effects and also influence the air quality and climate change. However, there is little attention to high time-resolved variations and potential role of aerosol proteins during haze pollution periods. In this study, PM₂.₅ samples were first collected by a medium flow sampler in autumn and winter in Xi'an, China. Then three high time-resolved monitoring campaigns during haze pollution periods were conducted to determine the evolving characteristics of total protein concentration and explore the interactive relationship between protein and other chemical compositions. The results showed that the average protein concentration in PM₂.₅ in Xi'an (5.46 ± 3.32 μg m⁻³) was higher than those in most cities of China, and varied by seasons and air pollution conditions. In particular, the protein concentration in PM₂.₅ increased with the increase of air quality index (AQI). The continuous variations of aerosol proteins during the haze pollution periods further showed that PM₂.₅, atmospheric humidity and long-distance air mass transport exerted the significant impacts on the protein components in aerosols. Based on the present observation, it is suggested that aerosol proteins might affect the generation of secondary aerosols under haze weather conditions. The present results may provide a new possible insight into the variations and the role of aerosol proteinaceous matter during the formation and development of haze pollution.

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.

This study modified a passive sampling technique similar to the US EPA Method 325 A/B method but extended to include more toxic volatile organic compounds (VOCs) under varied climate conditions to enhance field applicability. A mixing chamber was built to determine uptake rates (Us) for the target compounds. It was found that the Us of 27 air toxics previously reported in the literature agreed reasonably well with our findings within 18%, thus proving the chamber's integrity. To broaden the compound coverage, both Carbopack X and Carboxen 569 were studied for a suite of toxic VOCs to meet stringent quality control (QC) criteria of correlation coefficients (R-square), method detection limits (MDL), back diffusion (BD), storage stability, as well as a wide range of climate conditions in temperature and humidity. After excluding the species that failed to pass any of the QC criteria, Carbopack X was found to fit 50 air toxics, whereas Carboxen 569 held 37. After excluding the overlapped species, 61 toxic VOCs can be determined with robust Us for a broad range of climate conditions when the two sorbents are used in pairs. A one-week field measurement was conducted to compare with the online thermal desorption gas chromatography-mass spectrometry (TD-GC-MS) with hourly data resolution. The field passive sampling showed comparable results to the means of the online hourly measurements, despite the high variability of selected target compounds, such as toluene from 0.3 ppbv as the 5th percentile to the maximum of about 80 ppbv. Passive sampling clearly demonstrated the ability to smooth out concentration variability and thus the time-averaging strength of toxic VOCs, revealing its ideal role as an exposure monitor over time. The passive sampling method can be more desired than active sampling or online methods when the aim is simply the knowledge of prolonged time-averaged concentrations.