Land surface and air temperature dynamics: The role of urban form and seasonality

Multi-scale correlation reveals the evolution of socio-natural contributions to tropospheric HCHO over China from 2005 to 2022

Monitoring the spatiotemporal distribution of formaldehyde (HCHO) is crucial for reducing volatile organic compounds (VOCs) emissions, and the long-term evolution of socio-natural sources contributions to tropospheric HCHO over China is still unclear. We propose an oversampling algorithm for quantitatively tracking the evolution of uncertainty, which lowers the uncertainty of the original Level 2 OMI HCHO data (50 % -105 %) to 0-50 %, and then we examine the evolution of contributions from various emissions sources applying multi-scale correlation. We found that the high formaldehyde vertical column densities (VCD) caused by human activities in eastern China are crossing the Hu Huanyong Line, which was formerly used to demarcate the population distribution. National-scale analysis indicate that HCHO VCD are significantly correlated with per capita Gross Domestic Product (per capita GDP) (r = 0.948) and Normalized Difference Vegetation Index (r = 0.864), while no substantial correlation with land surface temperature (LST) (r = 0.233). A valuable finding at city-scale is that the vast majority of cities exhibits clear latitude zoning characteristics in the correlation between HCHO VCD and per capita GDP. Diagnosis at pixel scale reveals that anthropogenic emissions continue to weaken the contributions of emissions caused by the increase in vegetation proportion. NDVI = 0.8 is the critical characteristic point where the contribution of natural source exceeds that of anthropogenic sources, while the point presents a decreasing trend in recent years due to the enhancement of human activities levels. Rise in LST over vegetation areas show positive driving effect on formaldehyde emissions, but continuous urbanization is diminishing this contribution. NDVI = 0.8 is a characteristic point to determine whether the contribution proportion of regional surface temperature to formaldehyde emissions from vegetation begun to rise. Our research identifies the evolutionary process and characteristics of the spatiotemporal distribution and socio-nature sources contributions of tropospheric formaldehyde of China from 2005 to 2022.

An improved machine learning-based model for prediction of diurnal and spatially continuous near surface air temperature

Near-surface air temperature (Tair) is crucial for assessing urban thermal conditions and their impact on human health. Traditional Tair estimation methods, reliant on sparse weather stations, often miss spatial variability. This study proposes a novel framework using a federated learning artificial neural network (FLANN) for fine-scale Tair prediction. Leveraging spatially complete thermal data from Landsat 8/9, Sentinel 3, and Himawari 8/9 (105 acquisition days, 2013-2023), and data from automatic weather stations, 23 predictor variables were extracted. After rigorous selection processes, nine variables significantly correlated with Tair were identified. Comparative analysis against established machine learning and linear models, using cross-validation data, showed FLANN's superior performance with a Pearson correlation coefficient (r) of 0.98 and a root mean square error (RMSE) of 0.97 K, compared to r and RMSE of 0.85 and 1.09, respectively, for the linear model. FLANN showed greater improvements for urban stations with r and RMSE differences of 0.19 and - 2.03 K. Application of FLANN to predict Tair in Hong Kong in July 2023 enabled detailed urban heat island (UHI) analysis, revealing dynamic spatial and temporal UHI patterns. This study highlights FLANN's potential for accurate Tair prediction and UHI analysis, enhancing urban thermal environment management.

Towards more equitable cooling services of urban parks: Linking cooling effect, accessibility and attractiveness

Global warming and rapid urbanization have caused frequent occurrences of heat waves and urban heat island effect, presenting a significant threat to health of urban residents. Researches have indicated that cooling services provided by parks are essential in alleviating impact of heat wave events and urban heat island effect. However, previous researches on park cooling services center around cooling effect, with a lack of exploration regarding the fairness of such services. To fill this gap, this study quantifies the level of equity in cooling services in 18 parks in the core area of Hangzhou. Through this study, we hope to clarify the current situation of fairness of cooling services in urban parks and provide fairer park cooling services through scientific and reasonable park layouts. This will alleviate the threat of rapid urbanization and climate change to urban residents, and make the urban environment develop in a more livable direction. We assessed the cooling effect using remote sensing and the ArcGIS platform to screen parks with cooling effect and to quantify their cooling service efficiency. We utilized spatial network analysis to quantify the accessibility and origin-destination matrix data to quantify the attractiveness to reflect the level of park cooling services. The results reveal that 18 parks exhibit a noticeable cooling effect, albeit with variations observed among parks. The percentage of urban parks with low accessibility is 77.80%, indicating that the distribution of accessible space presents an uneven status quo. In addition, 72.20% of parks have low attractiveness of cooling services, indicating that some parks have insufficient attractiveness of cooling services. Based on each indicator of cooling services, we categorize urban parks into four types based on supply and demand, and propose adaptive planning measures and intervention strategies to provide a reference for equitable distribution of cooling services in urban parks.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

Demographic disparity in diurnal surface urban Heat Island exposure across local climate zones: A case study of Chongqing, China

Surface urban heat island (SUHI) exposure significantly harms human health during rapid urbanization. Identifying the areas and demographic groups under high SUHI exposure is critical for mitigating heat-related hazards. However, despite broad concern in US-European countries, rare studies discuss the diurnal SUHI exposure of demographic subgroups across Local Climate Zones (LCZs) in Chinese cities. Therefore, taking Chongqing as the case study, we measured the diurnal SUHI exposure of demographic subgroups (e.g., gender, age, and income) across different LCZs (compact, open, and sparsely-built zones) by coupling the ECOSTRESS data and mobile phone signaling data. The results indicated that Chongqing's compact high/middle-rise zones suffered a higher SUHI exposure due to high land surface temperature (LST) and a larger size of population than open zones. Despite a relatively low population density, extremely high LST in compact low-rise zones (e.g., industrial parks) contributes to considerable accumulated SUHI exposure. The SUHI exposure risk exhibited the differences between daytime and nighttime, resulting from SUHI variation and population flow. The demographic analysis showed that Chongqing's demographic subgroups are exposed disproportionately to SUHI. Elderly groups suffered relatively high exposure in compact high-rise zones. Low-incomers witnessed a high exposure in open zones. These findings call for alleviating SUHI exposure risk by targeting vulnerable groups and high-intensity exposure areas.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Persistent Organic Pollutants and Suspended Particulate Matter in Snow of Eastern Siberia in 2009-2023: Temporal Trends and Effects of Meteorological Factors and Recultivation Activities at Former Industrial Area

Suspended particulate matter (SPM), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCP) were studied in the snow cover at urban and suburban localities in the Irkutsk region, Eastern Siberia for their temporal variations in 2009-2023, daily deposition fluxes (DDFs), and effects of some meteorological factors, as well as the effects of different technogenic activities in the industrial area of the former organochlorine enterprises of Usol'ekhimprom. SPM loads at both stations were found to be at a low level of pollution. The levels of HCB, α + γ-HCH, and ∑p,p'-DDX were lower than Russian maximum permissible levels (MPLs) in drinking water, groundwater, and surface water for household drinking and cultural purposes. The sums of all organochlorine compounds studied in snow were higher than the MPL in freshwater water bodies for fishery purposes. The levels of the DDFs of HCHs, DDTs, and heptachlorinated PCB decreased, di- and trichlorinated PCB levels increased, and HCB levels changed at a polynomial line during 2009-2023. The change in the relative composition of PCBs was found as a result of recultivation activities at the industrial area of the former organochlorine enterprise of Usol'ekhimprom. The air humidity and temperature are the key meteorological factors affecting the DDFs of PCBs and OCPs.