High-density volatile organic compound monitoring network for identifying pollution sources

The elusive sources of air pollution have hampered effective control across all sectors, with long-term consequences for the greenhouse effect and human health. Multiple monitoring systems have been highly desired for locating the sources. However, when faced with extensive sources, diverse air environments and meteorological conditions, the low spatiotemporal resolution, poor reliability and high cost of existing monitors were significant obstacles to their applications. Extending our previous demonstration of sensitive and reliable electrochemical sensors, we here present a machine-learning-assisted sensor arrays for monitoring typical volatile organic compounds (VOCs), which shows the consistent response with gas chromatography–mass spectrometry in the actual air environment. As a proof-of-concept, a low-cost and high-resolution VOC network of 152 sets of monitors across ~55 km2 of mixed-used land is established in southwest Beijing. Benefiting from the strong reliability, the pollution sources are revealed by the VOC network and supported by the joint mobile sampling of a vehicle-mounted gas chromatography–mass spectrometry system. With the sustained help of the network, the sources polluted by the local industrial facilities, traffic, and restaurants are effectively site-specific abatement by the local authorities and enterprises during the next half-year. Our findings open up a promising path toward more effective tracing of regional pollution sources, as well as accelerate the long-term transformation of industry and cities. [Display omitted] •A sensitive and reliable sensor array is developed for VOC detection.•A monitor based on the sensor array shows the consistent response with GC–MS in the actual air environment.•A low-cost and high-resolution VOC network of 152 sets of SAMs across ~55 km2 of mixed-used land is established.•VOC network helps to reveal the polluted sources and effectively site-specific abatement.