Revealing nighttime construction-related activities from a spatially distributed air quality monitoring network†

In this study, through a novel network-based data-driven method, we reveal a likely unintended, nighttime-specific impact of construction activities on elevated coarse particulate matter (PM_c) concentrations in a metropolitan area. We analyzed the spatial and temporal patterns of coarse particulate matter (PM_c) levels in the urban part of a 165-node PM FEM monitoring network in Xi'an, China. We employed a novel technique called network analysis, which relies on data-driven, peer-to-peer comparisons within the monitoring network to identify regional events and local hotspots. Results revealed that the highest PM_c concentrations in the urban section of Xi'an occurred during late night and early morning. Aided by satellite-based aerial imagery and data mining of internet resources, we confirmed those peaks' strong association with construction-related sources. This observation is further supported by Land Use Regression (LUR) models, which demonstrate significant improvement in nighttime PMc prediction accuracy when they include a ‘construction site’ variable, an effect not observed during daytime. This finding underscores the significant impact of frequent nighttime construction activities and associated heavy-duty truck traffic (“dump trucks” responsible for transporting construction materials and wastes), which are likely unintentionally incentivized by both local policies and construction practices in many Chinese cities. Our work demonstrated the potential of utilizing air quality monitoring networks for construction-related environmental monitoring and enforcement. We also recommend that policymakers re-assess construction-related environmental and transportation policies by considering the trade-offs between air quality—the focus of our analysis—and other environmental and non-environmental considerations such as construction efficiency, traffic safety, noise, and waste management.