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There is limited information on contaminations of per- and polyfluoroalkyl substances (PFASs) in the water source affected by agricultural activities. The contamination of PFASs was investigated in the sources of agricultural groundwater and nearby surface water from an important vegetable production base supply for Beijing and Tianjin, located in North China. Σ13PFAS concentrations ranged from 0.321 to 8.285 ng/L, with an average concentration of 2.022 ng/L in the groundwater and 19.673 ng/L in the surface water. With the increase in the carbon chain length, the mean concentrations of PFASs in groundwater generally showed a decreasing trend. The dominated congeners of short-chain perfluorobutanesulfonate and perfluorooctanoate acid (PFOA) were detected in all groundwater samples, with mean concentrations of 0.944 and 0.654 ng/L. The difference was that PFOA and perfluorooctanesulfonate (PFOS) were the dominant congeners in nearby surface water, with concentrations of 7.585 and 3.421 ng/L. Thus, the concentrations of PFOA and PFOS in the surface water were about 8.5 times higher than those in the groundwater, indicating that Σ13PFAS concentrations might decrease with the water migration from the overground to the underground. In addition, significant correlations were observed between PFASs and DOC/TN related to agricultural activities, suggesting a certain relationship existed between PFAS conger distributions and organic carbon/nutrients in water. Health risk assessment indicated that the PFAS exposure caused insignificantly immediate harm to residents in the studied area. This survey provided information on the sources, spatial distribution, and potential migration characteristics of PFASs in a typical agricultural area of North China.

Emissions of agricultural machines during tillage processes played an important role in severe seasonal pollution events in agricultural areas in China and cannot be ignored. In this study, the CO, NOX, HC and PM2.5 emissions of agricultural machines during real-world tillage processes were tested using a portable emission measurement system (PEMS), and their fuel-based and tillage area-based emission factors were calculated. The CO, NOX, HC and PM2.5 emissions were influenced by emission standards, engine rated power, tillage processes and crops. Only the CO, HC and PM2.5 fuel-based emission factors were reduced from China 0 to China II. For China III agricultural machines, the fuel-based emission factors were higher during plowing and tilling than during harvesting. The tillage area-based CO, NOX, HC and PM2.5 emission factors of corn tillage process were 11.85 ± 8.30, 53.21 ± 48.80, 3.46 ± 3.14 and 1.64 ± 1.33 kg/km2, respectively. The tillage area-based CO, NOX, HC and PM2.5 emission factors of wheat tillage process were 19.69 ± 21.50, 79.98 ± 63.22, 3.90 ± 2.96 and 1.61 ± 2.43 kg/km2, respectively. The tillage area-based emission factors of China III agricultural machines during plowing and tilling were higher than those during harvesting. The fuel consumption per unit tillage area can be used to provide a reference for the interconversion of the two emission factors in future studies. By comparing the fuel-based emission factors in this study with those in the Guidelines and other studies, we observed that the CO, HC and PM2.5 emissions of agricultural machines with corresponding emission standards may be overestimated and the NOX emissions may be underestimated in areas where wheat and corn are mainly grown. Moreover, the pollutant emissions of agricultural machines were regionally different. These results could help elucidate the pollution contribution of agricultural machines in China.

A series of lockdown measures in response to the Corona Virus Disease 2019 (COVID-19) outbreak resulted in a drop in anthropogenic emissions and changes in concentrations of PM2.5 and O3. Backward trajectories analysis, cluster analysis, potential source contribution function (PSCF) and concentration weighted trajectory (CWT) technologies were conducted to reveal the characteristics and potential source areas of pollutants in Beijing before the COVID-19 outbreak (BCO period), during the outbreak (COB period) and after the outbreak (ACO period), as well as the contemporaneous period in 2019 (CCO period), which is critical for exploring the efficient control measures and making policy. The results indicated that despite the significant reduction in anthropogenic emissions during the epidemic, the PM2.5 concentrations increased by 1.0% caused by unfavorable meteorological conditions. O3 concentrations increased by 174.8% compared to that during the BCO period due to the increased temperature and inappropriate precursor reduction ratios. A considerable decrease of NO3- in PM2.5 was observed under the influence of significant reductions in vehicle emissions during the lockdown. The cluster analysis revealed that short-range transport played a significant role in the accumulation of local PM2.5 pollution, while long-range northwest airflows contributed more to O3 accumulation, and weakened as the season changed. The PSCF and CWT analysis demonstrated that potential source areas of PM2.5 were mostly located in the central and southern Hebei, the southwestern Shandong in the CCO period, and expanded to central Inner Mongolia and northern Shanxi in the COB period. These areas were highly compatible with the high emission areas of the emission inventory statistics. After the outbreak, the source areas of O3 were centered in the Beijing-Tianjin-Hebei region and Shandong province, with a radial dispersion in all directions, while they were distributed in the central Mongolia and Inner Mongolia during the other periods.