Urban and regional ozone (O₃) pollution is a public health concern and causes damage to ecosystems. Due to the diverse emission sources of O₃ precursors and the complex interactions of air dispersion and chemistry, identifying the contributing sources of O₃ pollution requires integrated analysis to guide emission reduction plans. In this study, the meteorological characteristics leading to O₃ polluted days (in which the maximum daily 8–h average O₃ concentration is higher than the China Class II National O₃ Standard (160 μg/m³)) in Guangzhou (GZ, China) were analyzed based on data from 2019. The O₃ formation regimes and source apportionments under various prevailing wind directions were evaluated using a Response Surface Modeling (RSM) approach. The results showed that O₃ polluted days in 2019 could be classified into four types of synoptic patterns (i.e., cyclone, anticyclone, trough, and high pressure approaching to sea) and were strongly correlated with high ambient temperature, low relative humidity, low wind speed, variable prevailing wind directions. Additionally, the cyclone pattern strongly promoted O₃ formation due to its peripheral subsidence. The O₃ formation was nitrogen oxides (NOₓ)-limited under the northerly wind, while volatile organic compounds (VOC)-limited under other prevailing wind directions. Anthropogenic emissions contributed largely to the O₃ formation (54–78%) under the westerly, southwesterly, easterly, southeasterly, or southerly wind, but only moderately (35–47%) under the northerly or northeasterly wind. Furthermore, as for anthropogenic contributions, local emission contributions were the largest (39–60%) regardless of prevailing wind directions, especially the local NOₓ contributions (19–43%); the dominant upwind regional emissions contributed 12–46% (e.g., contributions from Dongguan were 12–20% under the southeasterly wind). The emission control strategies for O₃ polluted days should focus on local emission sources in conjunction with the emission reduction of upwind regional sources.

In this study, the spatiotemporal variabilities and characteristics of ozone (O₃) and fine particulate matter (PM₂.₅) were reconstructed, and the interaction between meteorological conditions and the co-occurrence of O₃ and PM₂.₅ in Zhuhai, a city in the Pearl River Delta (China), was analysed. The vertical distributions of lower tropospheric O₃, aerosol extinction coefficient, and wind velocity were measured using a ground-based LiDAR system. The diurnal variations in air pollutant concentrations and meteorological conditions at ground level were examined from 28 November to December 8, 2020 considering the weather conditions in Zhuhai. Heavy pollution episodes with increased concentrations of O₃ and PM₂.₅ were observed from 6 to 7 December after a period of cold air invasion. The maximum hourly average concentrations of O₃ and PM₂.₅ at the ground level reached up to 190 μg/m³, 98 μg/m³, respectively. The horizontal wind speed rapidly decreased to less than 2 m/s during the heavy pollution episodes driven by O₃ and PM₂.₅, whereas the vertical wind velocity was dominated by the downdraught. When the large-scale synoptic winds were weak, a strengthening sea breeze in the afternoon could promote the landward propagation of warm marine air masses, and a lower surface wind speed was driven by the convergence of cold air from the north and warm air from the south. In turn, this increased the residence time of air pollutants and promoted their conversion to secondary pollutants. Regarding the pollution sources, the results indicated that the Pearl River Estuary represented a ‘pool’ of O₃ and PM₂.₅ pollution. In addition, the contribution of regional pollutant transport could not be ignored when considering the accumulative increase in air pollution. Overall, the relatively weak synoptic winds, low mixing height, and high generation of pollution around Zhuhai collectively resulted in high concentrations of O₃ and PM₂.₅.

Clearfield® wheat (Triticum aestivum) have helped eliminate the toughest grasses and broadleaf weeds in Spain since 2005. This crop production system includes other tolerant cultivars to the application of imidazolinone (IMI) herbicides. However, the continuous use and off-label rates of IMI herbicides can contribute to the development of resistance in Lolium rigidum and other weed species. In this research, the main objectives were to study the resistance mechanisms to acetolactate synthase (ALS) and acetyl coenzyme A carboxylase (ACCase) inhibitors in a L. rigidum accession (LrR) from a Clearfield® wheat field, with a long history rotating these IMI-tolerant crops and compare them with those present in the IMI-tolerant wheat. The resistance to ACCase inhibitors in LrR was due to point mutations (Ile1781Leu plus Asp2078Gly) of the target site gene plus an enhanced herbicide metabolism (EHM), on the other hand, in wheat accessions was due only by EHM. Mechanisms involved in the resistance to ALS inhibitors were both point mutations of the target gene and EHM in the IMI-tolerant wheat, while only evidence of mutation (Trp574Leu) was found in the multiple herbicide resistant L. rigidum accession. This research demonstrates that if crop rotation is not accompanied by the use of alternative sites of action in herbicide-tolerant crops, resistant weeds to herbicide to which crops are tolerant, can easily be selected. Moreover, repeated and inappropriate use of Clearfield® crops and herbicide rotations can lead to the evolution of multiple resistant weeds, as shown in this study, and have also inestimable environmental impacts.

Natural environments have been linked to decreased risk of cardiovascular disease (CVD) and respiratory disease (RSD) mortality. However, few cohort studies have looked at associations of natural environments with CVD or RSD hospitalization. The aim of this study was to evaluate these associations in a cohort of U.S. Medicare beneficiaries (∼63 million individuals). Our open cohort included all fee-for-service Medicare beneficiaries (2000–2016), aged ≥65, living in the contiguous U.S. We assessed zip code-level park cover based on the United States Geological Survey Protected Areas Database, average greenness (Normalized Difference Vegetation Index, NDVI), and percent blue space cover based on Landsat satellite images. Cox-equivalent Poisson models were used to estimate associations of the exposures with first CVD and RSD hospitalization in the full cohort and among those living in urban zip codes (≥1000 persons/mile²). NDVI was weakly negatively correlated with percent park cover (Spearman ρ = −0.23) and not correlated with percent blue space (Spearman ρ = 0.00). After adjustment for potential confounders, percent park cover was not associated with CVD or RSD hospitalization in the full or urban population. An IQR (0.27) increase in NDVI was negatively associated with CVD (HR: 0.97, 95%CI: 0.96, 0.97), but not with RSD hospitalization (HR: 0.99, 95%CI: 0.98, 1.00). In urban zip codes, an IQR increase in NDVI was positively associated with RSD hospitalization (HR: 1.02, 95%CI: 1.00, 1.03). In stratified analyses, percent park cover was negatively associated with CVD and RSD hospitalization for Medicaid eligible individuals and individuals living in low socioeconomic status neighborhoods in the urban population. We observed no associations of percent blue space cover with CVD or RSD hospitalization. This study suggests that natural environments may benefit cardiorespiratory health; however, benefits may be limited to certain contexts and certain health outcomes.

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM₂.₅) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002–1.006) and 0.3% (95% CI: 1.001–1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM₂.₅ and PM₁₀ were 2.2% (95% CI: 1.012–1.032) and 2.2% (95% CI: 1.012–1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Pyrite oxidation and sedimentary sulfate dissolution are the primary components of riverine sulfate (SO₄²⁻) and are predominant in global SO₄²⁻ flux into the ocean. However, the proportions of anthropogenic SO₄²⁻ inputs have been unclear, and their tempo-spatial variations due to human activities have been unknown. Thus, field work was conducted in a spatially heterogeneous human-affected area of the Yihe River Basin (YRB) during a wet year (2010) and drought years (2017/2018). Dual sulfate isotopes (δ³⁴S–SO₄²⁻ and δ¹⁸O–SO₄²⁻) and Bayesian isotope mixing models were used to calculate the variable anthropogenic SO₄²⁻ inputs and elucidate their temporal impacts on riverine SO₄²⁻ flux. The results of the mixing models indicated acid mine drainage (AMD) contributions increased from 56.1% to 83.1% of upstream sulfate and slightly decreased from 46.3% to 44.0% of midstream sulfate in 2010 and 2017/2018, respectively, in the Yihe River Basin. The higher upstream contribution was due to extensive metal-sulfide-bearing mine drainage. Sewage-derived SO₄²⁻ and fertilizer-derived SO₄²⁻ inputs in the lower reaches had dramatically altered SO₄²⁻ concentrations and δ³⁴S–SO₄²⁻ and δ¹⁸O–SO₄²⁻ values. Due to climate change, the water flow discharge decreased by about 70% between 2010 and 2017/2018, but the riverine sulfate flux was reduced by only about 58%. The non-proportional increases in anthropogenic sulfate inputs led to decreases in the flow-weighted average values of δ³⁴S–SO₄²⁻ and δ¹⁸O–SO₄²⁻ from 10.3‰ to 9.9‰ and from 6.1‰ to 4.4‰, respectively. These outcomes confirm that anthropogenic SO₄²⁻ inputs from acid mine drainage (AMD) have increased, but sewage effluents SO₄²⁻ inputs have decreased.

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM₁₀) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM₁₀ data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM₁₀ concentrations at 1 km × 1 km spatial resolution across the four provinces. An out-of-bag R² of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM₁₀ with a spatial CV R² of 0.48 and temporal CV R² of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.

Surface water quality monitoring programs have been developed to examine traditional contaminants, such as persistent organic pollutants (POPs). However, urbanization, which is increasing around the world, is increasing discharge of treated wastewater and raw sewage in many regions. Pharmaceuticals and their metabolites represent typical markers of such trajectories in urbanization. We selected an ongoing monitoring program, which was designed for routine surveillance of nonionizable POPs in different aquatic matrices, to examine the occurrence of 67 pharmaceuticals and their metabolites in water and multiple bioindicator matrices: benthic invertebrates, juvenile fish, and adult fish (plasma and muscle tissue) from ten river systems with varying levels of watershed development. In addition, we placed zebra mussels and passive samplers in situ for a fixed period. A statistically significant relationship between pharmaceutical levels in passive samplers and biota was found for caged zebra mussels and benthic invertebrates, while only a few pharmaceuticals were identified in fish matrices. Invertebrates, which have received relatively limited study for pharmaceutical bioaccumulation, accumulated more pharmaceuticals than fish, up to thirty different substances. The highest concentration was observed for sertraline in zebra mussels and telmisartan in benthic invertebrates (83 and 31 ng/g ww, respectively). Our results across diverse study systems indicate that ongoing surface water quality monitoring programs, which were originally designed for traditional organic pollutants, need to be revised to account for bioaccumulation dynamics of pharmaceuticals and other ionizable contaminants. Aquatic monitoring programs routinely examine accumulation of nonionizable organic pollutants; however, we identified that these efforts need to be revised to account for bioaccumulation of ionizable contaminants, which reached higher levels in invertebrates than in fish.

Tyre wear particles may be the largest source of microplastic to the natural environment, yet information on their biological impacts is inadequate. Two key estuarine invertebrates; the clam Scrobicularia plana and the ragworm Hediste diversicolor were exposed to 10% tyre particles in sediment for three days. Both species consumed the particles, although S. plana consumed 25x more than H. diversicolor (967 compared with 35 particles.g⁻¹ wet weight, respectively). We then investigated the impact of 21 days exposure to different concentrations of tyre particles in estuarine sediments (0.2, 1, and 5% dry weight sediment) on aspects of the health of S. plana and H. diversicolor. Reductions in feeding and burial rates were observed for S. plana but not H. diversicolor, whilst both species showed a decrease in protein content in response to the greatest tyre particle concentration (5%), linked to an 18% decrease in energy reserves for H. diversicolor. Five percent tyre particle exposure led to an increase in total glutathione in the tissues of H. diversicolor, whilst lipid peroxidation decreased in the digestive glands of S. plana, possibly due to an increase in cell turnover. This study found that S. plana's health was impacted at lower concentrations than H. diversicolor, likely due to its consumption of large quantities of sediment. At the high exposure concentration (5%), the health of both invertebrates was impacted. This study did not separate the effects caused by the microplastic particles versus the effects of the chemical additives leaching from these particles, but our results do indicate that future studies should investigate effects in isolation and in combination, to determine the main drivers of toxicity.

This study proposes a red tide risk assessment method based on intercriteria correlation (CRITIC), technique for order preference by similarity to an ideal solution (TOPSIS), assessment of estuarine trophic status (ASSETS) methods and Monte Carlo simulation (MCS) to calculate the probability of each risk level. The integrated TOPSIS-ASSETS method is used to calculate the risk levels of each year, where index weight is determined by CRITIC method. MCS method is employed to calculate the probability of each risk level. The results showed that level III to level V indicates high possibility of red tides in the case study area (Tolo Harbor). The highest risk rating was level V in 1988. The change of the risk level of red tide is consistent with the real situation of the occurrence of red tide. Another case of the east part of Skagerrak Strait shows that the results of this method are consistent with field situation. When there is an error between the evaluation results and the real situation, MCS can further suggest the probability of error in the evaluation results. Meanwhile, sensitivity analysis was used to test the performance of the evaluation model and two comparative methods. The results show that the proposed risk assessment method has better performance than other methods and can provide an effective risk evaluation for red tide management.