Month: September 2025

Our new paper, “Multi-parameterization of hydrological processes in an urban canopy model“, is published in Building and Environment (IF: 7.6).

The paper can be downloaded at https://www.sciencedirect.com/science/article/abs/pii/S036013232501039X.

Authors: Yuqi Huang, Chenghao Wang, & Zhi-Hua Wang

Abstract: Accurately representing urban hydrological processes is essential for understanding energy and water exchanges in cities, improving weather and climate simulations across scales, and informing effective flood and water resource management. Despite recent advancements in urban land surface modeling, many models still struggle to achieve a closed water balance and rely on oversimplified representation of hydrological processes. These issues primarily stem from the inherent complexity and heterogeneity of the urban hydrologic cycle. In this study, we integrated multiple hydrological parameterization schemes into a single-layer urban canopy model to better capture key processes such as canopy interception, surface runoff, soil moisture dynamics, and groundwater runoff. These new schemes enhance the model’s existing capabilities of resolving root water uptake and evapotranspiration. Evaluation against short-term, single-site observations shows that the proposed model improves the accuracy of surface energy and water partitioning, with RMSE reductions of 8 % for urban latent heat flux, 7 % for sensible heat flux, and 12 % for net radiation compared to the previous version. We further evaluated the influence of initial soil moisture on model performance and the cooling effect of urban trees. Results reveal that urban trees can either cool or warm the street canyon air depending on soil moisture availability. While shading effectively lowers ground surface temperature, evaporative cooling primarily reduces canyon air temperatures. Our findings underscore the importance of incorporating detailed hydrological processes into urban climate models, with broad implications for city planning, public health, and sustainable development efforts aimed at mitigating urban heat stress and water-related risks.

DOI: https://doi.org/10.1016/j.buildenv.2025.113567

Our new paper, “Heavy metal imprints in Antarctic snow from research and tourism“, is published in Nature Sustainability (IF: 27.1).

The paper can be downloaded at https://www.nature.com/articles/s41893-025-01616-7.

Authors: Raúl R. Cordero, Sarah Feron, Avni Malhotra, Alessandro Damiani, Minghu Ding, Francisco Fernandoy, Juan A. Alfonso, Belkis Garcia, Juan M. Carrera, Pedro Llanillo, Paul Wachter, Jaime Pizarro, Elise Roumeas, Edgardo Sepúlveda, Jose Jorquera, Chenghao Wang, Jorge Carrasco, Zutao Ouyang, Pedro Oyola, Maarten Loonen, Anne Beaulieu, Jacob Dana, Alia L. Khan, Gino Casassa, & Choong-Min Kang

Abstract: Antarctica, long regarded as one of the last pristine environments on Earth, is increasingly affected by human activity. As tourism surges and scientific operations expand, air pollution from local emissions is raising new environmental concerns. Here we analyse surface snow samples collected along a ~2,000-km transect, from the South Shetland Islands (62° S) to the Ellsworth Mountains (79° S), to map the geochemical fingerprints of aerosol deposition. We identify distinct spatial patterns shaped by crustal, marine, biogenic and anthropogenic sources. Notably, we detect heavy metal imprints in the snow chemistry of the northern Antarctic Peninsula, where major research stations are concentrated and marine tourism traffic is most intense. Our findings shed light on the extent of the impacts from energy-intensive local activities in Antarctica, underscoring the need for enhanced environmental monitoring and sustainable management strategies in this fragile region.

DOI: https://doi.org/10.1038/s41893-025-01616-7

Our new paper, “Regional mapping of natural gas compressor stations in the United States and Canada using deep learning on satellite imagery“, is published in Journal of Environmental Management (IF: 8.4).

The paper can be downloaded at https://www.sciencedirect.com/science/article/pii/S0301479725027045.

Authors: Benjamin Liu, Jeremy Irvin, Mark Omara, Chenghao Wang, Gil Kornberg, Hao Sheng, Ritesh Gautam, Andrew Y. Ng, & Robert B. Jackson

Abstract: A comprehensive, open-access database of oil and gas infrastructure locations is necessary for accurately attributing emissions from satellites and managing pollution impacts on surrounding communities. However, open-access datasets are limited for many infrastructure types, including natural gas compressor stations, which account for approximately one-third of U.S. oil and gas sector methane emissions and are associated with harmful pollution. Here, we developed the first automated deep learning approach for detecting natural gas compressor stations in satellite imagery. We experimented with various neural network architectures trained on different image resolutions and footprints, and found that the best model achieved a precision of 0.81 at 0.95 recall. Incorporating whether a proposed facility is close to an oil and gas pipeline further improved model precision by 0.02. Deploying the best model to identify facilities across a critical 200,000 km² oil and gas-producing region capturing the Marcellus Shale, we detected 1103 compressor stations that were not previously reported in a large bottom-up oil and gas infrastructure database. Incorporating these new locations revealed that population exposure to potential emitted pollutants may be underestimated by as much as 74 % when relying exclusively on reported data. Our work highlights the utility of machine learning to enhance infrastructure mapping for environmental management and pollution assessment.

DOI: https://doi.org/10.1016/j.jenvman.2025.126728