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To achieve a holistic understanding of the intricate interactions among human activities, atmospheric chemistry, and acid rain in China, a rigorous analysis of rainwater chemistry was made using a dataset comprising 2656 data points from 24 sites. The main cation and anion in the chemical composition of precipitation were Ca2+ and SO42− in China, with an average concentration of 169.9 μeq/L and 135.4 μeq/L, respectively. Acid rain generally occurs in southern cities such as Shenzhen, Guangzhou, Zhuhai, Xiamen, and Chongqing. There were evident regional disparities in acidity and ion concentrations in rainwater, with an increase in acidity and a decrease in ion concentrations from north to south across China. Utilizing positive matrix factorization, the study found that NH4+, SO42−, and NO3− mainly originated from anthropogenic sources such as fossil fuel combustion, vehicle exhaust emissions, agricultural fertilization, and industrial emissions (as reflected by F3 and F4). Ca2+ mainly stems from crustal factors, including industrial dust and natural crust (as represented by F1 and F4). Na+ and Cl− were traceable from marine sources (as reflected by F5), while Mg2+ originated from crust origin (as presented by F1). K+ was mainly derived from a mixed source of crust, marine, and biomass burning (as indicated by F2 and F3). The correlation analyses showed that SO42− and NO3− showed significant correlations with GDP and population. F− was associated with wastewater, which may be linked to the production of brick and tiles from clay with high fluoride contents. The pH was negatively related to industrial wastewater. Long-term analysis of precipitation chemistry in four cities suggested a clear decrease in the proportion of SO42− but a considerable increase in the proportion of NO3− in anions in metropolitans of Shanghai and Chongqing due to the environmental measures that targeted reducing sulfur dioxide (SO2) emissions and increase of vehicles. This showed that pollution control strategies had an impact on precipitation ion concentrations. These results can conclude that economic and industrial growth, which will increase energy consumption, utilization of coal combustion, and a subsequent rise in pollutant emissions, can contribute to the change in the chemical compositions of rainwater and the exacerbation of acid rain. [ABSTRACT FROM AUTHOR]

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products. [ABSTRACT FROM AUTHOR]

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions. [ABSTRACT FROM AUTHOR]