Prediction of the oxidation potential of PM2.5 exposures from pollutant composition and sources

Associations of prenatal exposure to PM2.5 and its components with offsprings' neurodevelopmental and behavioral problems: A prospective cohort study from China

Prenatal exposure to fine particulate matter (PM2.5) has been linked with increased neurodevelopmental disorders. However, the most detrimental component of PM2.5 and the most vulnerable exposure time windows remain undetermined, especially in areas with high PM2.5 levels. In a prospective cohort study involving 4494 mother-child dyads, we examined the associations of prenatal exposure to PM2.5 and its four main components with children's neurodevelopmental and behavioral problems (NBPs), separately in three pregnancy trimesters. Poisson regression and generalized additive models were used to depict the linear and nonlinear associations, respectively. Weighted quantile sum and Bayesian kernel machine regression models were applied to examine the effects of exposure to both mixed and individual components. Results showed that exposure to PM2.5 and its components throughout the three trimesters increased the risk of children's NBPs (Risk ratio for PM2.5: 1.16, 95 % confidence interval 1.14-1.18 per μg/m3 in the first trimester; 1.15, 1.12-1.17 in the second trimester; 1.06, 1.04-1.08 in the third trimester), with associations gradually diminishing as pregnancy progressed (P values for trends < 0.05). Among the four main components of PM2.5, exposure to SO42- posed the highest risks on children's NBPs, while organic matter contributed the largest proportion to the overall impacts of PM2.5 exposure. These results underscore the significance of mitigating PM2.5 exposure in pregnant women to reduce the risk of neurodevelopmental disorders in offspring. Our findings would inform risk assessment of PM2.5 exposure and facilitate the development of precision preventive strategies targeting specific components of PM2.5 in similar areas with high levels of exposure.

Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated historical and future PM2.5 concentrations and associated OP using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. Considering different future socio-economic pathways and emission scenario assumptions, we quantified how the contribution from various anthropogenic emission sectors will change under these scenarios. Results show that compared to the CESM_SSP2-4.5_CLE scenario (based on moderate radiative forcing and Current Legislation Emission), the CESM_SSP1-2.6_MFR scenario (based on sustainability development and Maximum Feasible Reductions) is projected to yield greater environmental and health benefits in the future. Under the CESM_SSP1-2.6_MFR scenario, annual average PM2.5 concentrations (OP) are expected to decrease to 30 (0.8 nmolmin-1m-3) in almost all regions by 2030, which will be 65 % (67 %) lower than that in 2010. From a long-term perspective, it is anticipated that OP in the Fen-Wei Plain region will experience the maximum reduction (82.6 %) from 2010 to 2049. Largely benefiting from the effective control of PM2.5 in the region, it has decreased by 82.1 %. Crucially, once emission reduction measures reach a certain level (in 2040), further reductions become less significant. This study also emphasized the significant role of secondary aerosol formation and biomass-burning sources in influencing OP during both historical and future periods. In different scenarios, the reduction range of OP from 2010 to 2049 is estimated to be between 71 % and 85 % by controlling precursor emissions involved in secondary aerosol formation and emissions from biomass burning. Results indicate that strengthening the control of anthropogenic emissions in various regions are key to achieving air quality targets and safeguarding human health in the future.

Chemical and oxidative properties of fine particulate matter from near-road traffic sources

The toxicity associated with the fine particulate matter (PM2.5) has not been well studied, particularly in relation to the emissions from on-road vehicles and other sources in low- and middle-income countries such as India. Thus, a study was conducted to examine the oxidative potential (OP) of PM2.5 at a roadside (RS) site with heavy vehicular traffic and an urban background (BG) site in Mumbai using the dithiothreitol (DTT) assay. Simultaneous gravimetric PM2.5 was measured at both sites and characterized for carbonaceous constituents and water-soluble trace elements and metals. Results depicted higher PM2.5, elemental carbon (EC), and organic carbon (OC) concentrations on the RS than BG (by a factor of 1.7, 4.6, and 1.2, respectively), while BG had higher water-soluble organic carbon (WSOC) levels (by a factor of 1.4) and a higher WSOC to OC ratio (86%), likely due to the dominance of secondary aerosol formation. In contrast, the measured OPDTTv at RS (8.9 ± 5.5 nmol/min/m3) and BG (8.1 ± 6.4 nmol/min/m3) sites were similar. However, OPDTTv at BG was higher during the afternoon, suggesting the influence of photochemical transformation on measured OPDTTv at BG. At RS, OC and redox-active metals (Cu, Zn, Mn, and Fe) were significantly associated with measured OP (p < 0.05), while at BG, WSOC was most strongly associated (p < 0.05). The coefficient of divergence (COD) for PM2.5, its chemical species, and OPDTTv was >0.2, indicating spatial heterogeneity between the sites, and differences in emission sources and toxicity. The estimated hazard index (HI) was not associated with OPDTTv, indicating that current PM2.5 mass regulations may not adequately capture the health effects of PM2.5. The study highlights the need for further studies examining PM2.5 toxicity and developing toxicity-based air quality regulations.

Seasonal variation of driving factors of ambient PM2.5 oxidative potential in Shenzhen, China

Reactive oxygen species (ROS) play a central role in health effects of ambient fine particulate matter (PM_2.5). In this work, we screened for efficient and complementary oxidative potential (OP) measurements by comparing the response values of multiple chemical probes (OP^DTT, OP^OH, OP^GSH) to ambient PM_2.5 in Shenzhen, China. Combined with meteorological condition and PM_2.5 chemical composition analysis, we explored the effects of different chemical components and emission sources on the ambient PM_2.5 OP and analyzed their seasonal variations. The results show that OP_m^DTT(mass-normalized) and OP_m^GSH-SLF were highly correlated (r = 0.77). OP^DTT was mainly influenced by organic carbon, while OP^OH was highly dominated by heavy metals. The combination of OP^DTT and OP^OH provides an efficient and comprehensive measurement of OP. Temporally, the OPs were substantially higher in winter than in summer (1.4 and 4 times higher for OP_m^DTT and OP_m^OH, respectively). The long-distance transported biomass burning sources from the north dominated the OP^DTT in winter, while the ship emissions mainly influenced the summer OP. The OP_m^DTT increased sharply with the decrease of PM_2.5 mass concentration, especially when the PM_2.5 concentration was lower than 30 μg/m³. The huge differences in wind fields between the winter and summer cause considerable variations in PM_2.5 concentrations, components, and OP. Our work emphasizes the necessity of long-term, multi-method, multi-component assessment of the OP of PM_2.5.

Recent advances in carbon-based nanomaterials for the treatment of toxic inorganic pollutants in wastewater

Wastewater contains inorganic pollutants, generated by industrial and domestic sources, such as heavy metals, antibiotics, and chemical pesticides, and these pollutants cause many environmental problems.

Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm

Ozone is one of the most important air pollutants, with significant impacts on human health, regional air quality and ecosystems. In this study, we use geographic information and environmental information of the monitoring site of 5577 regions in the world from 2010 to 2014 as feature input to predict the long-term average ozone concentration of the site. A Bayesian optimization-based XGBoost-RFE feature selection model BO-XGBoost-RFE is proposed, and a variety of machine learning algorithms are used to predict ozone concentration based on the optimal feature subset. Since the selection of the underlying model hyperparameters is involved in the recursive feature selection process, different hyperparameter combinations will lead to differences in the feature subsets selected by the model, so that the feature subsets obtained by the model may not be optimal solutions. We combine the Bayesian optimization algorithm to adjust the parameters of recursive feature elimination based on XGBoost to obtain the optimal parameter combination and the optimal feature subset under the parameter combination. Experiments on long-term ozone concentration prediction on a global scale show that the prediction accuracy of the model after Bayesian optimized XGBoost-RFE feature selection is higher than that based on all features and on feature selection with Pearson correlation. Among the four prediction models, random forest obtained the highest prediction accuracy. The XGBoost prediction model achieved the greatest improvement in accuracy.