Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county-specific blood lead levels in China from 1980 to 2040 using a machine learning model. Blood lead data from China were sourced from 1180 surveys published between 1980 and 2022. Additionally, regional statistical figures for 15 natural and socioeconomic variables were obtained or estimated as predictors. A machine learning model, using the random forest algorithm and 2973 generated samples, was created to predict county-specific blood lead levels in China from 1980 to 2040. Geometric mean blood lead levels in children (i.e., age 14 and under) decreased significantly from 104.4 μg/L in 1993 to an anticipated 40.3 μg/L by 2040. The number exceeding 100 μg/L declined dramatically, yet South Central China remains a hotspot. Lead exposure is similar among different groups, but overall adults and adolescents (i.e., age over 14), females, and rural residents exhibit slightly lower exposure compared to that of children, males, and urban residents, respectively. Our predictions indicated that despite the general reduction, one-fourth of Chinese counties rebounded during 2015-2020. This slower decline might be due to emerging lead sources like smelting and coal combustion; however, the primary factor driving the decline should be the reduction of a persistent source, legacy gasoline-derived lead. Our approach innovatively maps lead exposure without comprehensive surveys.
Keywords: China; blood lead levels; lead poisoning; machine learning; random forest algorithm.