Access to safe drinking water is one of the fundamental human rights and an important part of healthy living. This study considered various land use methods, used geostatistical analysis, and triangular random model to explore nitrogen pollution and estimate its potential risk to human health for local populations in Songnen Plain of Northeast China and recognize parameter uncertainties. Nitrate concentrations in groundwater ranged from 0.01 to 523.45 mg/L, more than 72.35% of the samples exceeded Grade III threshold (20 mg/L of N) as per China's standard, and nitrate nitrogen content is greater than 20 mg/L accounted for around 60% of the research area, mainly distributed in the eastern and central high plain area. The nitrate-nitrogen content of groundwater in the town land was significantly higher than that of agricultural land, and the ammonia nitrogen content was conversely. The townland's risk value was two times that of agricultural land, considering different land use methods would avoid overestimating or underestimating regional risk value. Non-carcinogenic risks (HI) of two land use were above the safety level (i.e., HI > 1), suggesting that groundwater nitrate would have significant health effects on the age groups, and further threaten children. There was a wide range of fluctuations in the uncertainty of nitrogen concentration and model evaluation parameters; triangular random model was more sensitive to data changes, which could reduce the uncertainty. The contribution rate of nitrate-nitrogen concentration to risk was above 90%, which explained the need for random sampling to improve the evaluation results reliability. The findings in this paper will provide new insight for solving uncertainties in water safety management.

Road dust, which contains trace elements and certain organic matter that can be harmful to human health, plays an important role in atmospheric pollution. In this paper, concentrations of 16 elements in the road dust of Changchun, China were determined experimentally. A total of 100 samples were collected using plastic brushes and dustpans, and the elements were analyzed by an inductively coupled plasma optical emission spectrometer (ICP-OES). It was indicated that the elements could be divided into major and trace elements. The concentration of trace elements followed the trend: mercury (Hg) > manganese (Mn) > zinc (Zn) > lead (Pb) > chromium (Cr) > copper (Cu) > vanadium (V) > arsenic (As) > nickel (Ni) > cobalt (Co) > cadmium (Cd). Contamination-level-assessment calculated by the geo-accumulation index (I_geo) showed that the pollution-level ranged from non-contaminated to extreme contamination, while the calculations of enrichment factor (EF) showed that EF values exhibited a decreasing trend: Cd > Hg > As > Pb > Cu > Co > Zn > Ni > Cr > V > Mn > Mg > Fe > Sr > Ba. In our study, ingestion was the greatest exposure pathway for humans to intake trace elements by calculating the average daily dose (ADD) from three routes (ingestion, inhalation, and dermal contact). According to the health risk assessment results, the non-carcinogenic risks that human beings suffered from these elements were insignificant. Additionally, the hazard quotient (HQ) values were approximately one-tenth in the case of children. Meanwhile, the total excess cancer risk (ECR) was also lower than the acceptable level (10⁻⁶–10⁻⁴) for both adults and children.

The source area of Liao River is a typical cold region in northeastern China, which experiences serious problems with agricultural nonpoint source pollution (NPS), it is important to understand future climate change impacts on NPS in the watershed. This issue has been investigated by coupling semi distributed hydrological model (SWAT), statistical downscaling model (SDSM) and global circulation model (GCMs). The results show that annual average temperature would rise by 2.1 °C (1.3 °C) in the 2080 s under scenario RCP8.5 (RCP4.5), and annual precipitation would increase by 67 mm (33 mm). The change in winter temperature and precipitation is most significant with an increase by 0.23 °C/10a (0.17 °C/10a) and 1.94 mm/10a (2.78 mm/10a). The future streamflow, TN and TP loads would decrease by 19.05% (10.59%), 12.27% (8.81%) and 10.63% (6.11%), respectively. Monthly average streamflow, TN and TP loads would decrease from March to November, and increase from December to February. This is because the increased precipitation and temperature in winter, which made the spring snowpack melting earlier. These study indicate the trends of nonpoint source pollution during the snowmelt period under climate change conditions, accordingly adaptation measures will be necessary.