Resolution of the Ongoing Challenge of Estimating Nonpoint Source Neonicotinoid Pollution in the Yangtze River Basin Using a Modified Mass Balance Approach.
Yuanchen ChenLu ZangGuofeng ShenMaodian LiuWei DuJie FeiLiyang YangLong ChenXuejun WangWeiping LiuMeirong ZhaoPublished in: Environmental science & technology (2019)
Neonicotinoid insecticides have been widely consumed worldwide, particularly in China. There is a growing interest in the environmental research community about the occurrence, fates, sources, and risks of neonicotinoids. Nine neonicotinoids in river/lake water were measured at 12 sites along the Yangtze River Basin during the dry and wet seasons in 2016, and nonpoint sources were also identified based on a modified mass balance method. A significantly higher concentration of neonicotinoids was found during the dry season probably due to the dilution effect and insecticide consumption. The high pollution levels are due to posing high ecological risks compared with the recommended thresholds. In 2016, 1190 (95% confidence interval (CI) = 822-1690) tons of neonicotinoids were transferred into the adjacent sea. Nonpoint source pollution (1700 (CI = 1200-2370) tons) was the major contributor (91.3%) to the total input of neonicotinoids into the system. Composition profiles identifying specific neonicotinoid sources indicated some changes in usage patterns from old to new types of neonicotinoids. This spatial and seasonal field study and source identification is expected to fill the data gap regarding the limited information on neonicotinoid use patterns and to inform further effective policy-making and intervention programs in China that should be urgently promoted in the near future.
Keyphrases
- human health
- risk assessment
- heavy metals
- water quality
- drinking water
- particulate matter
- climate change
- health risk assessment
- public health
- healthcare
- mental health
- randomized controlled trial
- electronic health record
- big data
- aedes aegypti
- health information
- artificial intelligence
- social media
- deep learning
- solid phase extraction
- zika virus
- bioinformatics analysis