Optimizing sampling strategy for Chinese National Sewage Sludge Survey (CNSSS) based on urban agglomeration, wastewater treatment process, and treatment capacity

Abstract

As a sink and source of contaminants, sewage sludge is a good matrix to capture the spatial-temporal trend of chemicals and assess the potential risks these chemicals pose to human health and the environment. In order to understand these chemical risks, a robust statistical sewage sludge sampling strategy for Chinese wastewater treatment plants (WWTPs) must be designed. The purpose of this paper is to develop such a sampling strategy for Chinese WWTPs which may be used optimally. Before creating the sampling design, the distribution of WWTPs was categorically analyzed. These categories include urban agglomeration, wastewater treatment process, and wastewater treatment capacity. Particular attention was given to the studying of population distribution, gross domestic product, WWTP number, wastewater treatment flow, and dry sludge production in each urban agglomeration. In addition, correlation analysis was conducted among these five indexes. Due to the heterogeneity of WWTPs, stratified sampling had to be used to homogenize the sampling units. The eight strategies proposed herein were based on simple random sampling and stratified random sampling methods. Moreover, the aforementioned three categories (urban agglomeration, treatment process, and treatment capacity) were intended to be stratification indicators. Furthermore, Monte Carlo simulations revealed that the treatment capacity based stratified random sampling strategy (Strategy 4) results in the optimal sample representation, with the smallest root mean square error compared to seven other sampling strategies with different strata. This optimal stratified sampling strategy, if employed during the Chinese national sewage sludge survey, has the potential to greatly contribute to data quality and assurance.