Category: New Publications Page 1 of 4

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

Our new paper, “Cities as hotspots of compound heat and fine particulate matter pollution: A 23-year urban–rural comparison across the United States“, is published in Environmental Research (IF: 7.7).

The paper can be downloaded at https://www.sciencedirect.com/science/article/abs/pii/S0013935125017608.

Authors: Jessica Leffel, Chenghao Wang, Xiao-Ming Hu, Sarah Feron, & Sarah Henry

Abstract: Heat stress and fine particulate matter (PM_2.5) pollution are major stressors that threaten public health and environmental quality. When heat waves and PM_2.5 pollution episodes co-occur as compound events, their impacts intensify, often leading to increased mortality and morbidity. This study provides a 23-year (2000–2022) analysis of heat waves, PM_2.5 pollution episodes, and their compound occurrences during the warm season across urban and surrounding rural areas of the contiguous U.S. using reconstructed daily minimum air temperature and PM_2.5 datasets. Results show that urban areas generally experienced more frequent, prolonged, and intense events than rural surroundings. Specifically, 98.8 % of cities had more frequent heat waves, 88.6 % experienced longer durations, and 98.8 % were more intense, primarily due to the nighttime urban heat island effect. Similarly, 85.4 % of cities had more frequent PM_2.5 pollution episodes, 52.8 % experienced longer durations, and 80.5 % exhibited higher cumulative pollution intensity, largely driven by urban emissions. Compound heat and PM_2.5 pollution episodes were more frequent and intense in ∼98 % of urban areas, with more than half experiencing longer durations. The spatial patterns of compound events closely resembled those of PM_2.5 pollution episodes, suggesting that air pollution plays a dominant role in their occurrence. Notably, after a declining trend through ∼2016, the number of PM_2.5 pollution and compound event days increased in the western U.S. in recent years due to rising wildfire emissions. These findings highlight the heightened environmental risks experienced by urban populations and emphasize the need for city- and region-specific heat and pollution mitigation strategies.

DOI: https://doi.org/10.1016/j.envres.2025.122508

Our new paper, “Building resilient urban water systems: emerging opportunities for solving long-lasting challenges“, is published in Hydrological Sciences Journal (IF: 2.5).

The paper can be downloaded at https://www.tandfonline.com/doi/full/10.1080/02626667.2025.2529267.

Authors: Bertil Nlend et al.

Abstract: In this perspective paper, we analyse the challenges and opportunities of hydrology in the urban context and propose solutions for innovation and sustainability by leveraging advancements across technology, society, and governance for resilient cities. Technological breakthroughs, such as smart sensors and artificial intelligence, can enhance the efficiency and resilience of real-time water monitoring and predictions. Public awareness and community engagement can foster behavioural change and empower residents to actively participate in urban water governance through initiatives like rainwater harvesting and participatory planning. Additionally, big data and remote sensing provide cities with the insights needed for adaptive, data-driven decision-making. Together, these developments represent a paradigm shift from reactive problem-solving to proactive, integrated solutions that prioritise equity, environmental health, and urban resilience. Finally, the paper highlights the differences in progress between the Global North and the Global South and proposes research priorities for the future of urban hydrology.

DOI: https://doi.org/10.1080/02626667.2025.2529267

Our new paper, “Compound heat and ozone pollution in the urban environment“, is published in Urban Climate (IF: 6.9).

The paper can be downloaded at https://doi.org/10.1016/j.uclim.2025.102511.

Authors: Chenghao Wang, Xiao-Ming Hu, Sarah Feron, Jessica Leffel, & Raúl R. Cordero

Abstract: Ground-level ozone pollution and extreme heat are closely linked environmental stressors that often peak during similar warm-season conditions. Their co-occurrence as compound events can significantly amplify negative health impacts, particularly in densely populated urban areas. In this study, we systematically characterized the frequency, duration, and cumulative intensity of warm-season compound heat and ozone pollution events across all urban areas and their rural surroundings in the contiguous U.S. (CONUS), using long-term, high-resolution daily air temperature and pollution datasets. We found that urban heat waves, defined using daily maximum air temperature, were generally more frequent, more intense, and longer lasting than their rural counterparts, primarily due to the daytime urban heat island effect. In contrast, over half of the U.S. cities experienced fewer, less intense, and shorter ozone pollution episodes than nearby rural areas, largely reflecting differences in ozone chemical regimes. Despite these contrasting patterns, compound heat and ozone pollution events were more frequent in 88.8 % of urban areas, with higher cumulative heat and ozone pollution intensities in 91.1 % and 88.1 % of cities, respectively. However, compound event durations tended to be shorter in urban environments. These findings highlight the dependence of such compound events on local factors such as precursor emissions, as well as background conditions such as regional meteorological patterns, emphasizing the need for tailored mitigation strategies to simultaneously reduce heat stress and ozone pollution. This study also lays the foundation for detailed regional numerical simulations to elucidate the mechanisms that drive urban–rural disparities during these compound events.

DOI: https://doi.org/10.1016/j.uclim.2025.102511

Our new paper, “Ultrafine-resolution urban climate modeling: Resolving processes across scales“, is published in Journal of Advances in Modeling Earth Systems (IF: 4.6).

The paper can be downloaded at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2025MS005053.

Authors: Chenghao Wang, Yongling Zhao, Qi Li, Zhi-Hua Wang, & Jiwen Fan

Abstract: Recent advances in urban climate modeling resolution have improved the representation of complex urban environments, with large-eddy simulation (LES) as a key approach, capturing not only building effects but also urban vegetation and other critical urban processes. Coupling these ultrafine-resolution (hectometric and finer) approaches with larger-scale regional and global models provides a promising pathway for cross-scale urban climate simulations. However, several challenges remain, including the high computational cost that limits most urban LES applications to short-term, small-domain simulations, uncertainties in physical parameterizations, and gaps in representing additional urban processes. Addressing these limitations requires advances in computational techniques, numerical schemes, and the integration of diverse observational data. Machine learning presents new opportunities by emulating certain computationally expensive processes, enhancing data assimilation, and improving model accessibility for decision-making. Future ultrafine-resolution urban climate modeling should be more end-user oriented, ensuring that model advancements translate into effective strategies for heat mitigation, disaster risk reduction, and sustainable urban planning.

DOI: https://doi.org/10.1029/2025MS005053

Our new paper, “Enhancing climate-driven urban tree cooling with targeted nonclimatic interventions“, is published in Environmental Science & Technology (IF: 10.9).

The paper can be downloaded at https://pubs.acs.org/doi/10.1021/acs.est.4c14275.

Authors: Zhaowu Yu, Siheng Li, Wenjun Yang, Jiquan Chen, Mohammad A. Rahman, Chenghao Wang, Wenjuan Ma, Xihan Yao, Junqi Xiong, Chi Xu, Yuyu Zhou, Jike Chen, Kangning Huang, Xiaojiang Gao, Rasmus Fensholt, Qihao Weng, & Weiqi Zhou

Abstract: Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We found that for every 10% increase in tree cover, surface temperatures are reduced by 0.25 °C during the day and 0.04 °C at night. Trees in humid regions exhibit the highest daytime CE, while those in arid zones demonstrate the greatest cooling effect at night. This can be explained by the difference in canopy density between the humid and arid zones. During the day, the high canopy density in the humid zone converts more solar radiation into latent heat flux. At night, the low canopy density in the arid zone intercepts less longwave radiation, which favors surface cooling. While climatic factors contribute nearly twice as much to CE as nonclimatic ones, our findings suggest that optimizing CE is possible by managing variables within specific thresholds due to their nonlinear effects. For instance, we revealed that in arid regions, an impervious surface coverage of approximately 60% is optimal, whereas in humid areas, reducing it to around 40% maximizes cooling benefits. These insights underscore the need for targeted management of nonclimatic factors to sustain tree cooling benefits and offer practical guidance for designing climate-resilient, nature-based urban strategies.

DOI: https://doi.org/10.1021/acs.est.4c14275

Our new paper, “Satellite-driven evidence of forest-induced temperature variability and its biophysical and biogeochemical pathways across latitudes“, is published in Ecological Indicators (IF: 7.0).

The paper can be downloaded at https://www.sciencedirect.com/science/article/pii/S1470160X25004753.

Authors: Zhaowu Yu, Mingchuan Shao, Wenjuan Ma, Chenghao Wang, & Jiachuan Yang

Abstract: Forests significantly influence local temperature dynamics, although the specifics of their impacts and mechanisms exhibit global variability. This study investigates the cooling or warming effects of global forests from 2001 to 2021 using multi-satellite data. The results indicate that (1) boreal forests exhibit a significant warming effect of +1.99 °C. Temperate forests exhibit nighttime warming but notable daytime cooling effect, resulting in a net daily cooling effect (−0.48 °C in the northern hemisphere, −0.91 °C in the southern hemisphere). The daily cooling effects peak in summer and gradually rise from spring to autumn, with winter exhibiting a warming inclination. Tropical forests consistently provide a cooling effect year-round (−2.11 °C). (2) Over the study period, tropical forests consistently revealed robust and stable cooling effects. Temperate forests displayed modest fluctuations in cooling effects, while the warming effect of boreal forest showed a slow trend upwards at a rate of +0.03 °C per year. (3) The warming effect of boreal forests is primarily due to NEE (net ecosystem exchange) and ET pathways (indirect effect: +0.253 and +0.392), while tropical forest cooling is driven by increased evapotranspiration (indirect effect: −0.938). As for temperate zones, annual cooling is primarily led by the NEE pathway (NH: −0.055 and SH: −0.415). (4) A robust annual coherence emerges between forests’ temperature regulation effects and ΔNEE, ΔET, and Δalbedo, where augmented ET and albedo significantly amplify cooling effects synchronously. The decrease in NEE exhibits a positive but non-synchronous impact on cooling at the local scale, while showing a strong and synchronous relationship with ΔLST at the global scale. These findings highlight the crucial role of forests in local temperature regulation, necessitating targeted management strategies.

DOI: https://doi.org/10.1016/j.ecolind.2025.113545

Our new paper, “Observation and simulation of methane plumes during the morning boundary layer transition“, is published in Journal of Geophysical Research: Atmospheres (IF: 3.8).

The paper can be downloaded at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JD042317.

Authors: Xiao-Ming Hu, Wesley Honeycutt, Chenghao Wang, Binbin Weng, Bowen Zhou, & Ming Xue

Abstract: Methane (CH₄) contributes significantly to global warming. However, accurate identification of CH₄ sources for reducing CH₄ emissions is often hampered by inadequate accuracy and spatiotemporal coverage of CH₄ detection, and lack of accurate CH₄ forward modeling used in top-down inversion systems. In this study, a field experiment was conducted in Pampa, Texas using two CH₄ sensors (LI-COR and OGI camera) to detect CH₄ releases. We investigated whether high-resolution simulations using the Weather Research and Forecasting (WRF) model with greenhouse gases (WRF-GHG) could accurately simulate the CH₄ plumes in the presence of evolving atmospheric boundary layer from sunrise to noon. CH₄ plumes showed substantial variation in time. At a release rate of ∼17.5 kg hr⁻¹, the maximum enhancement of CH₄ measured by LI-COR was 2.6 ppm at sunrise (7:36 a.m.), 250 m from the release location. Within half an hour after sunrise, this enhancement decreased to 0.3–0.4 ppm. The enhancement was 0.2 ppm by 10:00 a.m. and further dropped to less than 0.1 ppm after 11:30 a.m. Due to the low temperature at sunrise, the OGI camera failed to detect the CH₄ plume. The WRF-GHG large-eddy simulation (LES) with 32 m grid spacing successfully reproduced these CH₄ enhancements. In situ measurements together with numerical simulations illustrate the impact of the transition from a stable boundary layer in the early morning to a convective boundary layer at noon on the dispersion of CH₄ plumes. Additionally, CH₄ plumes from a cattle farm in Oklahoma are briefly examined using the same modeling approach.

DOI: https://doi.org/10.1029/2024JD042317