Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations.

Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.

Is the virus-laden standing water change the transmission intensity of SARS-CoV-2 after precipitation? A framework for empirical studies.

Understanding the relationship between precipitation and SARS-CoV-2 is significant for combating COVID-19 in the wet season. However, the causes for the variation of SARS-CoV-2 transmission intensity after precipitation is unclear. Starting from "the Zhengzhou event," we found that the virus-laden standing water formed after precipitation might trigger some additional routes for SARS-CoV-2 transmission and thus change the transmission intensity of SARS-CoV-2. Then, we developed an interdisciplinary framework to examine whether the health risk related to the virus-laden standing water needs to be a concern. The framework enables the comparison of the instant and lag effects of precipitation on the transmission intensity of SARS-CoV-2 between city clusters with different formation risks of the virus-laden standing water. Based on the city-level data of China between January 01, 2020, and December 31, 2021, we conducted an empirical study. The result showed that in the cities with a high formation risk of the virus-laden standing water, heavy rain increased the instant transmission intensity of SARS-CoV-2 by 6.2% (95%CI: 4.85-10.2%), while in the other cities, precipitation was uninfluential to SARS-CoV-2 transmission, revealing that the health risk of the virus-laden standing water should not be underestimated during the COVID-19 pandemic. To reduce the relevant risk, virus-laden water control and proper disinfection are feasible response strategies.