Mapping the daily nitrous acid (HONO) concentrations across China during 2006-2017 through ensemble machine-learning algorithm

High-Resolution Spatiotemporal Modeling for PM2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies

Distinguishing the effects of different fine particulate matter components (PMCs) is crucial for mitigating their effects on human health. However, the sparse distribution of locations where PM is collected for component analysis makes it challenging to investigate the relevant health effects. This study aimed to investigate the agreement between data-fusion-enhanced exposure assessment and site monitoring data in estimating the effects of PMCs on gestational diabetes mellitus (GDM). We first improved the spatial resolution and accuracy of exposure assessment for five major PMCs (EC, OM, NO3-, NH4+, and SO42-) in the Pearl River Delta region by a data fusion model that combined inputs from multiple sources using a random forest model (10-fold cross-validation R2: 0.52 to 0.61; root mean square error: 0.55 to 2.26 μg/m3). Next, we compared the associations between exposures to PMCs during pregnancy and GDM in a hospital-based cohort of 1148 pregnant women in Heshan, China, using both site monitoring data and data-fusion model estimates. The comparative analysis showed that the data-fusion-based exposure generated stronger estimates of identifying statistical disparities. This study suggests that data-fusion-enhanced estimates can improve exposure assessment and potentially mitigate the misclassification of population exposure arising from the utilization of site monitoring data.

Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China

China is one of the largest producers and consumers of coal in the world. The National Action Plan on Air Pollution Prevention and Control in China (2013-2017) particularly aimed to reduce emissions from coal combustion. Here, we show whether the acute health effects of PM2.5 changed from 2013 to 2018 and factors that might account for any observed changes in the Beijing-Tianjin-Hebei (BTH) and the surrounding areas where there were major reductions in PM2.5 concentrations. We used a two-stage analysis strategy, with a quasi-Poisson regression model and a random effects meta-analysis, to assess the effects of PM2.5 on mortality in the 47 counties of BTH. We found that the mean daily PM2.5 levels and the SO42- component ratio dramatically decreased in the study period, which was likely related to the control of coal emissions. Subsequently, the acute effects of PM2.5 were significantly decreased for total and circulatory mortality. A 10 μg/m3 increase in PM2.5 concentrations was associated with a 0.16% (95% CI: 0.08, 0.24%) and 0.02% (95% CI: -0.09, 0.13%) increase in mortality from 2013 to 2015 and from 2016 to 2018, respectively. The changes in air pollution sources or PM2.5 components appeared to have played a core role in reducing the health effects. The air pollution control measures implemented recently targeting coal emissions taken in China may have resulted in significant health benefits.

Primary sources of HONO vary during the daytime: Insights based on a field campaign

Nitrous acid (HONO) is an established precursor of hydroxyl (OH) radical and has significant impacts on the formation of PM2.5 and O3. Despite extensive research on HONO observation in recent years, knowledge regarding its sources and sinks in urban areas remains inadequate. In this study, we monitored the atmospheric concentrations of HONO and related pollutants, including gaseous nitric acid and particulate nitrate, simultaneously at a supersite in downtown Chengdu, a megacity in southwestern China during spring, when was chosen due to its tolerance for both PM2.5 and O3 pollution. Furthermore, we employed the random forest model to fill the missing data of HONO, which exhibited good predictive performance (R2 = 0.96, RMSE = 0.36 ppbv). During this campaign, the average mixing ratio of HONO was measured to be 1.0 ± 0.7 ppbv. Notably, during periods of high O3 and PM2.5 concentrations, the mixing ratio of HONO was >50 % higher compared to the clean period. We developed a comprehensive parameterization scheme for the HONO budget, and it performed well in simulating diurnal variations of HONO. Based on the HONO budget analysis, we identified different mechanisms that dominate HONO formation at different times of the day. Vehicle emissions and NO2 heterogeneous conversions were found to be the primary sources of HONO during nighttime (21.0 %, 30.2 %, respectively, from 18:00 to 7:00 the next day). In the morning (7:00-12:00), NO2 heterogeneous conversions and the reaction of NO with OH became the main sources (35.0 %, 32.2 %, respectively). However, in the afternoon (12:00-18:00), the heterogeneous photolysis of HNO3 on PM2.5 was identified as the most substantial source of HONO (contributing 52.5 %). This study highlights the significant variations in primary HONO sources throughout the day.

Quantifying the Nitrogen Sources and Secondary Formation of Ambient HONO with a Stable Isotopic Method

Nitrous acid (HONO) is a reactive gas that plays an important role in atmospheric chemistry. However, accurately quantifying its direct emissions and secondary formation in the atmosphere as well as attributing it to specific nitrogen sources remains a significant challenge. In this study, we developed a novel method using stable nitrogen and oxygen isotopes (δ15N; δ18O) for apportioning ambient HONO in an urban area in North China. The results show that secondary formation was the dominant HONO formation processes during both day and night, with the NO2 heterogeneous reaction contributing 59.0 ± 14.6% in daytime and 64.4 ± 10.8% at nighttime. A Bayesian simulation demonstrated that the average contributions of coal combustion, biomass burning, vehicle exhaust, and soil emissions to HONO were 22.2 ± 13.1, 26.0 ± 5.7, 28.6 ± 6.7, and 23.2 ± 8.1%, respectively. We propose that the isotopic method presents a promising approach for identifying nitrogen sources and the secondary formation of HONO, which could contribute to mitigating HONO and its adverse effects on air quality.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Prediction of phosphorus concentrations in shallow groundwater in intensive agricultural regions based on machine learning

Excessive accumulation of phosphorus in soil profiles has become the main source of phosphorus in groundwater due to the application of phosphorus fertilizers in intensive agricultural regions (IARs). Elevated phosphorus concentrations in groundwater have become a global phenomenon, which places enormous pressure on the safe use of water resources and the safety of the aquatic environment. Currently, the prediction of pollutant concentrations in groundwater mainly focuses on nitrate nitrogen, while research on phosphorus prediction is limited. Taking the IARs approximately 8 plateau lakes in the Yunnan-Guizhou Plateau as an example, 570 shallow groundwater samples and 28 predictor variables were collected and measured, and a machine learning approach was used to predict phosphorus concentrations in groundwater. The performance of three machine learning algorithms and different sets of variables for predicting phosphorus concentrations in shallow groundwater was evaluated. The results showed that after all variables were introduced into the model, the R², RMSE and MAE of support vector machine (SVM), random forest (RF) and neural network (NN) were 0.52-0.60, 0.101-0.108 and 0.074-0.081, respectively. Among them, the SVM model had the best prediction effect. The clay content and water-soluble phosphorus in soil and soluble organic carbon in groundwater had a high contribution to the prediction accuracy of the model. The prediction accuracy of the model with reduced number of variables showed that when the number of variables was equal to 6, the RF model had R², RMSE and MAE values of 0.53, 0.108 and 0.074, respectively, and the number of variables increased again; there were small changes in R², RMSE and MAE. Compared with the SVM and NN models, the RF model can achieve higher accuracy by inputting fewer variables.

Constructing High-Resolution (10 km) Daily Diffuse Solar Radiation Dataset across China during 1982–2020 through Ensemble Model

Diffuse solar radiation is an essential component of surface solar radiation that contributes to carbon sequestration, photovoltaic power generation, and renewable energy production in terrestrial ecosystems. We constructed a 39-year (1982–2020) daily diffuse solar radiation dataset (CHSSDR), using ERA5 and MERRA_2 reanalysis data, with a spatial resolution of 10 km through a developed ensemble model (generalized additive models, GAM). The validation results, with ground-based measurements, showed that GAM had a high and stable performance with the correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE) for the sample-based cross-validations of 0.88, 19.54 Wm−2, and 14.87 Wm−2, respectively. CHSSDR had the highest consistency with ground-based measurements among the four diffuse solar radiation products (CERES, ERA5, JiEA, and CHSSDR), with the least deviation (MAE = 15.06 Wm−2 and RMSE = 20.22 Wm−2) and highest R value (0.87). The diffuse solar radiation values in China range from 59.13 to 104.65 Wm−2, with a multi-year average value of 79.39 Wm−2 from 1982 to 2020. Generally, low latitude and low altitude regions have larger diffuse solar radiation than high latitude and high altitude regions, and eastern China has less diffuse solar radiation than western China. This dataset would be valuable for analyzing regional climate change, photovoltaic applications, and solar energy resources. The dataset is freely available from figshare.

Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method

A non-parametric ensemble model was proposed to estimate the long-term (2015-2019) particle surface area concentrations (S_A) over China for the first time on basis of a vilification dataset of measured particle number size distribution. This ensemble model showed excellent cross-validation R² value (CV R² = 0.83) as well as a relatively low root-mean-square error (RMSE = 195.0 μm²/cm³). No matter in which year, considerable spatial heterogeneity of S_A was found over China with higher S_A in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Middle Lower Reaches of Yangtze River (MLYR). From 2015 to 2019, S_A significantly decreased in representative city clusters. The reduction rates were 140.1 μm²·cm^-3·a^-1 in BTH, 110.7 μm²·cm^-3·a^-1 in Pearl River Delta (PRD), 105.2 μm²·cm^-3·a^-1 in YRD, and 92.4 μm²·cm^-3·a^-1 in Sichuan Basin (SCB), respectively. Even though such quick reduction, high S_A (ranged from ~800 μm²/cm³ to ~1750 μm²/cm³) during the heavy pollution period (PM_2.5 > 75 μg/m³) still existed in the above-mentioned city clusters and may provide rich reaction vessels for multiphase chemistry. A dichotomy of enhanced annual 4th maximum daily 8-h average O₃ concentrations (4MDA8 O₃) and decreased S_A during summertime was found in Shanghai, a representative city of YRD. In Chengdu (SCB), increased 4MDA8 O₃ concentration was associated with a synchronous increase of S_A from 2017 to 2019. Differently, 4MDA8 O₃ concentrations enhanced in Beijing (BTH) and Guangzhou (PRD), while not significant for S_A before 2018. This work will greatly deepen our understanding of the historical variation and spatial distributions of S_A over China.

Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China

PM_2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM_2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM_2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM_2.5 were explored. The LTX model performed well with R² = 0.88, RMSE of 8.73 μg/m³ and MAE of 5.85 μg/m³. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM_2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM_2.5 values, which exhibit similar distribution trends to those of the observed PM_2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM_2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.