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Pharmaceuticals and Personal Care Products (PPCPs) are inadvertently released into the aquatic environment, causing detrimental effects on aquatic ecosystem. There is an urgent need of an in-deep investigation on contamination information of PPCPs in aquatic environment as well as the ecological risks to the aquatic ecosystem. This study was carried out in Lipu River basin, China, to investigate the distribution pattern and ecological risks of PPCPs. Results showed that PPCPs pollution is ubiquitous, 29 out of 30 targeted PPCPs were detected in Lipu River. Fourteen PPCPs were detected with a frequency of 100% in all water samples, and ten PPCPs were detected with a frequency of more than 80%. The cumulated PPCPs concentrations ranged from 33.30 ng/L to 99.60 ng/L, with a median value of 47.20 ng/L in Lipu River. Caffeine, flumequine, nifedipine, and lomefloxacin were the predominant PPCPs in study area. Caffeine showed high ecological risk, five and seven individual PPCP showed medium and low ecological risk to algae. [Display omitted] • 29 out of 30 targeted PPCPs were detected in the Lipu River basin. • CAF and FQ are the main PPCP compounds. • CAF exhibited high ecological risk to algae. • Rural area showed severe PPCPs contamination. [ABSTRACT FROM AUTHOR]

Route topography is an important test boundary of real driving emission (RDE) tests. However, the RDE test boundaries, such as atmospheric environment, driver behavior, route topography, and traffic congestion, are random, uncertain, and completely coupled. It is difficult to know to what extent route topography can determine on-road emissions, especially in a region with hilly topography. In this regard, the neural network predictor importance algorithms were proposed to measure the importance of the route topography test boundary. Based on tens of thousands of data window samples from the RDE tests in Chongqing, factor analysis was performed to reduce the data dimensionality and eliminate information overlap, and neural network models were established to predict pollutant emissions and calculate the relative importance of input variables. The results show that route topography is comparable to trip dynamics for on-road emissions but the importance of the route topography test boundary is not fully appreciated in the existing RDE regulation, making mountain cities suffer from severe vehicle emissions that are not effectively controlled. • Route topography impact on vehicle emissions was explored by neural network models. • Predictor importance algorithms using sensitivity analysis were introduced. • Data dimensions were downsized to eliminate the overlap of trip dynamic variables. • Data windows were treated as trip sections to expand the sample space significantly. • Route topography test boundary requires revision in the RDE test regulation. [ABSTRACT FROM AUTHOR]