Evaluation of MERRA-2 Precipitation Products Using Gauge Observation in Nepal

Modeling of mercury deposition in India: evaluating emission inventories and anthropogenic impacts

Mercury (Hg), a ubiquitous atmospheric trace metal posing serious health risks, originates from natural and anthropogenic sources. India, the world's second-largest Hg emitter and a signatory to the Minamata Convention, is committed to reducing these emissions. However, critical gaps exist in our understanding of the spatial and temporal distribution of Hg across the vast Indian subcontinent due to limited observational data. This study addresses this gap by employing the GEOS-Chem model with various emission inventories (UNEP2010, WHET, EDGAR, STREETS, and UNEP2015) to simulate Hg variability across the Asian domain, with a specific focus on India from 2013 to 2017. Model performance was evaluated using ground-based GMOS observations and available literature data. Emission inventory performance varied across different observational stations. Hence, we employed ensemble results from all inventories. The maximum relative bias for Total Gaseous Mercury (TGM) and Gaseous Elemental Mercury (GEM; Hg0) concentrations is about ±20%, indicating simulations with sufficient accuracy. Total Hg wet deposition fluxes are highest over the Western Ghats and the Himalayan foothills due to higher rainfall. During the monsoon, the Hg wet deposition flux is about 65.4% of the annual wet deposition flux. Moreover, westerly winds cause higher wet deposition in summer over Northern and Eastern India. Total Hg dry deposition flux accounts for 72-74% of total deposition over India. Hg0 dry deposition fluxes are higher over Eastern India, which correlates strongly with the leaf area index. Excluding Indian anthropogenic emissions from the model simulations resulted in a substantial decrease (21.9% and 33.5%) in wet and total Hg deposition fluxes, highlighting the dominant role of human activities in Hg pollution in India.

Atmospheric deposition of microplastics in an urban conglomerate near to the foothills of Indian Himalayas: Investigating the quantity, chemical character, possible sources and transport mechanisms

The global apprehension regarding the ubiquitous presence of microplastics (MPs) and their associated health risks underscore a significant challenge. However, our understanding on the occurrence and characteristics of this emerging class of pollutants in the different environmental compartments remains limited. For instance, despite housing approximately 20-25% of the global population, the evidence of the atmospheric MPs in Indian Subcontinent is exceedingly rare. Hence, we for the first-time present data on the depositional flux, chemical composition, morphological features of the atmospheric MPs collected from the foothills of Indian Himalayas. The total number of MPs for the collected samples ranged from 65 to 752 particles, with an average of 317 ± 171 particles count. The average flux of atmospheric MPs was 2256 ± 1221 particles/m2/day and varied significantly from 462 particles/m2/day to 5346 particles/m2/day. The highest deposition (5346 particles/m2/day) of atmospheric MPs was recorded during the 3rd week of sampling, coinciding with the Diwali festival. Based on the visual characteristics, we determined that the size of MPs ranged from 67 to 2320 μm, with a predominant presence of smaller particles (<1200 μm), primarily composed of fragments and films/sheets. Raman spectroscopy indicated that the analyzed MPs were mainly composed of 4 different polymer types, including PE (46.8 ± 7.2 %), PP (20.9 ± 7.4 %), PS (15.6 ± 3.8 %) and PET (16.7 ± 9.9 %). We further highlighted the extent to which climate variables control the deposition of atmospheric MPs in this urban conglomerate located in the foothills of Himalayas. Our Lagrangian parcel tracking approach showed that the greater frequencies are of local origin and clustered near to the studied region. We also speculate that atmospheric microplastics can be transported along the westerly winds. Though we did not observe any significant relation (p > 0.05) between meteorological parameters and the quantity of atmospheric MPs.

Assessing the accuracy and reliability of satellite-derived precipitation products in the Kosi River basin (India)

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.

Improving groundwater nitrate concentration prediction using local ensemble of machine learning models

Groundwater is one of the most important water resources around the world, which is increasingly exposed to contamination. As nitrate is a common pollutant of groundwater and has negative effects on human health, predicting its concentration is of particular importance. Ensemble machine learning (ML) algorithms have been widely employed for nitrate concentration prediction in groundwater. However, existing ensemble models often overlook spatial variation by combining ML models with conventional methods like averaging. The objective of this study is to enhance the spatial accuracy of groundwater nitrate concentration prediction by integrating the outputs of ML models using a local approach that accounts for spatial variation. Initially, three widely used ML models including random forest regression (RFR), k-nearest neighbor (KNN), and support vector regression (SVR) were employed to predict groundwater nitrate concentration of Qom aquifer in Iran. Subsequently, the output of these models were integrated using geographically weighted regression (GWR) as a local model. The findings demonstrated that the ensemble of ML models using GWR resulted in the highest performance (R2 = 0.75 and RMSE = 9.38 mg/l) compared to an ensemble model using averaging (R2 = 0.68 and RMSE = 10.56 mg/l), as well as individual models such as RFR (R2 = 0.70 and RMSE = 10.16 mg/l), SVR (R2 = 0.59 and RMSE = 11.95 mg/l), and KNN (R2 = 0.57 and RMSE = 12.19 mg/l). The resulting prediction map revealed that groundwater nitrate contamination is predominantly concentrated in urban areas located in the northwestern regions of the study area. The insights gained from this study have practical implications for managers, assisting them in preventing nitrate pollution in groundwater and formulating strategies to improve water quality.

Sulfur Dioxide (SO2) anthropogenic emissions distributions over Iraq (2000-2009) using MERRA-2 data

The Sulfur dioxide (SO2) is a colorless air pollutant cannot been seen with unaided eye. The fossil fuels burning, including coal, oil and gas, are the largest source of SO2. Often the SO2 Pollution reaches hazardous levels near the coal-fired plants, oil refineries, and in industrialized areas. This study analyzed the trend, spatial and temporal distributions of anthropogenic SO2 emissions in Iraq from January 2000 to December 2009, and series and trend analyses over six stations (Baghdad, Mosul, Basra, Muthanna, Babylon , and Kirkuk) using MERRA-2 data. The monthly SO2 are analyzed for the study period. The SO2 fluctuations were checked, depending on the background of each SO2 sources. The results shows clear reductions of SO2 values from 2002 till 2006, and the SO2 values increases during 2006 to 2009 over all stations. The annual trend analyses shows positive results over Baghdad, Al-Muthanna, and Babylon, and negative results over Basra, Mosul and Kirkuk. A large differences of SO2 values were over Basra, Kirkuk and Babylon, and slight difference over Baghdad, Mosul and Al-muthana. The monthly SO2 anthropogenic emissions values shows relatively stable over most stations, and the only fluctuation over Babylon and Kirkuk during study period. Observed higher SO2 values in the winter and spring than its values in the summer. This research pretends the satellites observation efficiently shows the spatial and temporal variations of SO2 for the considered study area

Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau

Remote sensing precipitation products provide rich data for ungauged basins. Evaluating the accuracy and detection capability of remote sensing precipitation products is crucial before application. In this study, an index system in terms of quantitative differences, capturing capacity and precipitation distribution was constructed to evaluate three precipitation products, TRMM 3B42 V7, GPM IMERGE Final and CMORPH V1.0, at various temporal and spatial scales on the Tibetan Plateau from 2001 to 2016. The results show that the correlations among the three products were larger at the monthly scale than at the annual scale. The lowest correlations between the products and observation data were found in December. GPM performed the best at the monthly and annual scales. Particularly, the GPM product presented the best capability of detection of both precipitation and non-precipitation events among the three products. All three precipitation products overestimated 0.1~1 mm/day precipitation, which occurred most frequently. An underestimation of precipitation at 10~20 mm/day was observed, and this intensity accounted for the majority of the precipitation. All three precipitation products showed an underestimation in terms of the annual maximum daily precipitation. The accuracy of the same product varied in different regions of the Tibetan Plateau, such as the south, the southeast, eastern–central region and the northeast, and there was a certain clustering of the accuracies of neighboring stations. GPM was superior to TRMM and CMORPH in the southern Tibetan Plateau, making it recommended for applications.

Capacity of Satellite-Based and Reanalysis Precipitation Products in Detecting Long-Term Trends across Mainland China

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.