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The incidence of aflatoxin M1 (AFM1) contamination in raw milk and its association with herd types was investigated in the ten provinces of Southern China in 2018. A total of 797 raw milk samples were collected from Zhejiang, Jiangxi, Jiangsu, Hunan, Hubei, Hainan, Guangxi, Guangdong, Fujian, and Anhui provinces. The herd types were classified according to their characteristics. Among the 797 samples, 401 were owned by large-scale farms (herd size >500 cows, for dairy cow farming), 303 were owned by milk processing plants (herd size >500 cows, for dairy cow farming + milk processing), and 93 were owned by small farm cooperatives (herd size ≤500 cows, for dairy cow farming). The results revealed the presence of AFM1 in 94 of the 797 samples (11.80%). None of them was above 500 ng/L (Chinese and US limit standard), whereas 0.88% were above 50 ng/L (EU limit standard). The AFM1 concentrations in samples non-compliant with the EU standard were 52, 61, 88, 89.5, 164, 240, and 486 ng/L, respectively. It is important to note that samples exceeding the EU limit were mainly found in the area of Jiangsu and Guangxi provinces, and the herd type of milk processing plants. Hence, it is necessary to monitor and enforce the regulatory compliance of AFM1 levels in the raw milk from these specific areas and herd type. A total of 797 raw milk samples from three herd types, were collected from ten provinces of Southern China in 2018. Ninety-four of the 797 raw milk samples (11.80%) were positive for AFM1, and none of them was above the Chinese and US legal limit, only 0.88% of samples were above the EU limit. All of the exceed samples defined by EU limit were found in the area of Jiangsu and Guangxi provinces and belonged to milk processing plants. [ABSTRACT FROM AUTHOR]

Systematic evaluations of the cumulative effects and mortality displacement of ambient particulate matter (PM) pollution on deaths are lacking. We aimed to discern the cumulative effect profile of PM exposure, and investigate the presence of mortality displacement in a large-scale population. We conducted a time-series analysis with different exposure-lag models on 13 cities in Jiangsu, China, to estimate the effects of PM pollution on non-accidental, cardiovascular, and respiratory mortality (2015–2019). Over-dispersed Poisson generalized additive models were integrated with distributed lag models to estimate cumulative exposure effects, and assess mortality displacement. Pooled cumulative effect estimates with lags of 0–7 and 0–14 days were substantially larger than those with single-day and 2-day moving average lags. For each 10 μg/m3 increment in PM 2.5 concentration with a cumulative lag of 0–7 days, we estimated an increase of 0.50 % (95 % CI: 0.29, 0.72), 0.63 % (95 % CI: 0.38, 0.88), and 0.50 % (95 % CI: 0.01, 1.01) in pooled estimates of non-accidental, cardiovascular, and respiratory mortality, respectively. Both PM 10 and PM 2.5 were associated with significant increases in non-accidental and cardiovascular mortality with a cumulative lag of 0–14 days. We observed mortality displacement within 30 days for non-accidental, cardiovascular, and respiratory deaths. Our findings suggest that risk assessment based on single-day or 2-day moving average lag structures may underestimate the adverse effects of PM pollution. The cumulative effects of PM exposure on non-accidental and cardiovascular mortality can last up to 14 days. Evidence of mortality displacement for non-accidental, cardiovascular, and respiratory deaths was found. • Maximum effect estimates were found at around 1-week cumulative PM exposure. • Cumulative PM exposure over 14 days increases all-cause and cardiovascular deaths. • PM exposure may primarily advance death among the frail subpopulation in China. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model for multi-objective optimization operation of multiple water sources under uncertainties was proposed. • Uncertainties in inflow and water demand forecasts can be considered simultaneously, and the correlation between uncertainties can be considered quantitatively. • The impact of inflow-water demand forecast uncertainties and their correlations on water resource operations are explored for water resources managers. Uncertainties in inflow and water demand forecasts bring risks to water resources management. Therefore, the research on multi-objective optimization operation of multiple water sources under uncertainties holds critical research significance and application value. This paper proposes a model for the multi-objective optimization operation of multiple water sources that can consider the inflow and water demand forecast dual uncertainties. In handling uncertainties, this paper adopts a scenario generation method based on joint distribution-Monte Carlo (JDMC), which allows for a quantitative consideration of the correlation between uncertainties. Moreover, the variable weight method and LINGO 20.0 are employed for solving multi-objective optimization issues to obtain stable Pareto frontiers. The proposed model is applied to the water receiving area inside Jiangsu Province of the South-to-North Water Transfer East Route Project (SNWTERP) in China. The main findings are as follows: 1) The impact of the inflow and water demand forecast uncertainties on the operation results is synergistic rather than antagonistic. Considering only a single uncertainty may result in water receiving area facing more severe water shortages or purchasing spot water at high prices. 2) There is a strong spatial correlation between uncertainties. If we ignore or only qualitatively consider the correlation between uncertainties, the impact of uncertainties on operation results will be underestimated or overestimated. 3) The Pareto frontiers are convex functions, indicating that decision-makers should choose a solution in the middle part. [ABSTRACT FROM AUTHOR]