Exploring the relationship between particulate matter, CO, SO2, NO2, O3 and urban heat island in Seoul, Korea

Towards Cleaner Air in Urban Areas: The Dual Influence of Urban Built Environment Factors and Regional Transport

Exposure to air pollution significantly elevates the risk of disease among urban populations. Improving city air quality requires not only traditional emission reduction strategies but also a focus on the intricate impacts of the urban built environment and meteorological elements. The complexity and diversity of factors within the urban built environment pose significant challenges to pollution control. This study employs machine learning to predict the spatial distribution of inhalable particulate matter (PM10) and fine particulate matter (PM2.5), integrating the clustering of pollutant-emitting enterprises and prevailing wind direction to trace pollutant sources. The results indicate that, compared to the multiple linear regression model, the R2 of the PM10 random forest prediction model improved from 0.64 to 0.88, while the RMSE decreased from 48.63 to 27.34. Similarly, the R2 of the PM2.5increased from 0.70 to 0.92, and the RMSE decreased from 30.85 to 15.31. High concentrations of PM10 and PM2.5 in Xi'an are primarily concentrated in the northeast and southwest of the central urban area. By integrating a kernel density analysis of polluting enterprises with the analysis of prevailing wind patterns, it is evident that particulate matter in Xi'an is substantially influenced by regional urban transport. Therefore, pollution control efforts must be enhanced through coordinated regional governance. According to the analysis results of the partial dependence plot, reducing winter temperature proves beneficial in reducing PM10 and PM2.5 levels. Effective measures encompass sprinkling and humidifying, reducing traffic emissions, and controlling various dust sources to lower PM10. Enhancing ventilation, increasing green spaces, and regulating vehicle and industrial emissions effectively reduce PM2.5. The study's findings offer a scientific foundation for administrative authorities to craft pollution reduction management policies and create adaptable territorial spatial planning. Moreover, they contribute to diminishing public exposure to pollution and improving the quality of public environmental health.

The combined effects of heatwaves, air pollution and greenery on the risk of frailty: a national cohort study

The associations between heatwaves and frailty, as well as the joint effects of heatwaves with air pollution and greenery, are currently unknown. This study leverages data from the China Health and Retirement Longitudinal Study (CHARLS), which collected information from 6,400 older adults between 2011 and 2018. Our outcome variable was frailty, as measured by the frailty index (FI > 0.21). Heatwaves were defined based on maximum temperature, incorporating four thresholds (≥ 97.5%, 97.5%, 92.5%, and 90%) and three durations (≥ 2, 3, and 4 days). These variables were considered as time-varying variables, representing the one-year exposure preceding survival events. Fine particulate matter (PM2.5) and greenery (normalized difference vegetation index (NDVI)) were utilized as indicators of air pollution and greenery exposure, respectively, and were treated as time-varying indicators concurrent with heatwaves.Time-varying Cox proportional hazards models were employed to assess the independent effects, as well as the multiplicative and additive interactions of heatwaves, air pollution, and greenery on the risk of frailty. These effects were quantified using hazard ratios (HRs), a traditional product term representing the ratio of HRs, and the relative excess risk due to interaction (RERI). Our findings indicate that heatwaves are associated with an increased risk of frailty, with HRs ranging from 1.035 (95% CI: 1.006-1.064) to 1.063 (95% CI: 1.028-1.101). We observed both a positive multiplicative interaction (HRs > 1) and an additive interaction (RERI > 0) between high level PM2.5 concentration, lack of greenery, and heatwaves. This study reveals that the combined effects exacerbate the adverse impact of heatwaves on the risk of frailty. Moreover, the combined effects of heatwaves, air pollution, and greenery exposure on frailty risk vary across age, gender, and educational attainment.

Atmospheric reactive nitrogen conversion kicks off the co-directional and contra-directional effects on PM2.5-O3 pollution

As the two important ambient air pollutants, particulate matter (PM2.5) and ozone (O3) can both originate from gas nitrogen oxides. In this study, applied by theoretical analysis and machine learning method, we examined the effects of atmospheric reactive nitrogen on PM2.5-O3 pollution, in which nitric oxide (NO), nitrogen dioxide (NO2), gaseous nitric acid (HNO3) and particle nitrate (pNO3-) conversion process has the co-directional and contra-directional effects on PM2.5-O3 pollution. Of which, HNO3 and SO2 are the co-directional driving factors resulting in PM2.5 and O3 growing or decreasing simultaneously; while NO, NO2, and temperature represent the contra-directional factors, which can promote the growth of one pollutant and reduce another one. Our findings suggest that designing the suitable co-controlling strategies for PM2.5-O3 sustainable reduction should target at driving factors by considering the contra-directional and co-directional effects under suitable sensitivity regions. For co-directional driving factors, the design of suitable mitigation strategies will jointly achieve effective reduction in PM2.5 and O3; while for contra-directional driving factors, it should be more patient, otherwise, it is possible to reduce one item but increase another one at the same time.

Examining air pollution exposure dynamics in disadvantaged communities through high-resolution mapping

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

Exploring airborne fungal contaminations and air quality pollution in nine ancient stone temples, Surin, Thailand

This study provided crucial insights into the concentrations of airborne fungi, environmental parameters, and atmospheric pollution in Thailand's ancient stone temples. Airborne fungi were identified based on morphological characteristics. Airborne fungi, meteorological parameters, and atmospheric pollutants concurrently assessed during each sampling event, evaluating indoor/outdoor ratio. Prevalent genera included Penicillium (14.36%), Aspergillus (10.94%), Cladosporium (10.74%), Rhizopus (6.31%), and Fusarium (5.90%), with an average fungi concentration of 4884.46 ± 724.79 CFU/m3. Eighteen fungal species out of the 64 airborne fungi identified were well-known serious pathogenic agents, contributing not only to structural deterioration but also to human health. Significant variations were observed between indoor and outdoor environments and across diverse landscapes, particularly for PM10 (ranging from 43.47 to 121.31 µg/m3) and PM2.5 (ranging from 29.59 to 89.60 µg/m3), with intensive incense burning identified as a prominent source of indoor atmospheric pollution. Historical temples, particularly situated in urban areas, were identified as significant reservoirs of airborne fungi. Correlations between meteorological parameters and pollutants revealed strong associations. Furthermore, principal component analysis (PCA) and cluster analysis elucidated distinct patterns in airborne fungal concentrations and contaminations. This study analyzed environmental factors, pollutants, airborne fungi, and geographical variations from July 2020 to March 2021. Understanding prevalent genera, airborne fungi concentrations, pathogenic species, biodeterioration, and environmental dynamics provided strategies for improving indoor air quality and mitigating airborne fungal contamination in archaeological buildings worldwide.

Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.

Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations

Nitrogen Dioxide (NO2 ) is a common air pollutant associated with several adverse health problems such as pediatric asthma, cardiovascular mortality,and respiratory mortality. Due to the urgent society's need to reduce pollutant concentration, several scientific efforts have been allocated to understand pollutant patterns and predict pollutants' future concentrations using machine learning and deep learning techniques. The latter techniques have recently gained much attention due it's capability to tackle complex and challenging problems in computer vision, natural language processing, etc. In the NO2 context, there is still a research gap in adopting those advanced methods to predict the concentration of pollutants. This study fills in the gap by comparing the performance of several state-of-the-art artificial intelligence models that haven't been adopted in this context yet. The models were trained using time series cross-validation on a rolling base and tested across different periods using NO2 data from 20 monitoring ground-based stations collected by Environment Agency- Abu Dhabi, United Arab Emirates. Using the seasonal Mann-Kendall trend test and Sen's slope estimator, we further explored and investigated the pollutants trends across the different stations. This study is the first comprehensive study that reported the temporal characteristic of NO2 across seven environmental assessment points and compared the performance of the state-of-the-art deep learning models for predicting the pollutants' future concentration. Our results reveal a difference in the pollutants concentrations level due to the geographic location of the different stations, with a statistically significant decrease in the NO2 annual trend for the majority of the stations. Overall, NO2 concentrations exhibit a similar daily and weekly pattern across the different stations, with an increase in the pollutants level during the early morning and the first working day. Comparing the state-of-the-art model performance transformer model demonstrate the superiority of ( MAE:0.04 (± 0.04),MSE:0.06 (± 0.04), RMSE:0.001 (± 0.01), R2 : 0.98 (± 0.05)), compared with LSTM (MAE:0.26 (± 0.19), MSE:0.31 (± 0.21), RMSE:0.14 (± 0.17), R2 : 0.56 (± 0.33)), InceptionTime (MAE: 0.19 (± 0.18), MSE: 0.22 (± 0.18), RMSE:0.08 (± 0.13), R2 :0.38 (± 1.35) ), ResNet (MAE:0.24 (± 0.16), MSE:0.28 (± 0.16), RMSE:0.11 (± 0.12), R2 :0.35 (± 1.19) ), XceptionTime (MAE:0.7 (± 0.55), MSE:0.79 (± 0.54), RMSE:0.91 (± 1.06), R2 : - 4.83 (± 9.38) ), and MiniRocket (MAE:0.21 (± 0.07), MSE:0.26 (± 0.08), RMSE:0.07 (± 0.04), R2 : 0.65 (± 0.28) ) to tackle this challenge. The transformer model is a powerful model for improving the accurate forecast of the NO2 levels and could strengthen the current monitoring system to control and manage the air quality in the region.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00754-z.

The Effect of Maternal Exposure to Air Pollutants and Heavy Metals during Pregnancy on the Risk of Neurological Disorders Using the National Health Insurance Claims Data of South Korea

The objective of this study was to evaluate the effects of high levels of maternal exposure to ambient air pollution and heavy metals on risks of autism spectrum disorder (ASD) and epilepsy using the National Health Insurance claims data of South Korea. The data of mothers and their newborns from 2016 to 2018 provided by the National Health Insurance Service were used (n = 843,134). Data on exposure to ambient air pollutants (PM2.5, CO, SO₂, NO₂, and O₃) and heavy metals (Pb, Cd, Cr, Cu, Mn, Fe, Ni, and As) during pregnancy were matched based on the mother's National Health Insurance registration area. SO₂ (OR: 2.723, 95% CI: 1.971-3.761) and Pb (OR: 1.063, 95% CI: 1.019-1.11) were more closely associated with the incidence of ASD when infants were exposed to them in the third trimester of pregnancy. Pb (OR: 1.109, 95% CI: 1.043-1.179) in the first trimester of pregnancy and Cd (OR: 2.193, 95% CI: 1.074-4.477) in the third trimester of pregnancy were associated with the incidence of epilepsy. Thus, exposure to SO₂, NO₂, and Pb during pregnancy could affect the development of a neurologic disorder based on the timing of exposure, suggesting a relationship with fetal development. However, further research is needed.

LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles

Landscape classification methods significantly impact the exploration of the mechanism of the relationship between landscapes and atmospheric particulate matter. This study compared the local climate zones (LCZs) and traditional land use/cover change (LUCC) landscape classification methods in explaining spatial differences in concentrations of atmospheric particulate matter (PM_2.5 and PM₁₀) and explored the mechanisms involved in how landscape elements affect atmospheric particulate matter. This was done by establishing a PM_2.5 and PM₁₀ land use regression (LUR) model of LCZ and LUCC landscapes under low, typical, and high particle concentration gradients in urban and suburban areas. The results show that under an LCZ classification system, the number of patches in the urban area of Shanghai was 548 times higher than that of a LUCC system. Moreover, LCZs were successfully established for LUR models in 12 scenarios, while only five models were established for LUCC, all of which were suburban models. The R² of the LUR model based on the LCZ landscape and atmospheric particulate matter was mostly higher than that of the LUCC. For unnatural landscapes, the LUCC demonstrated that an urbanized environment positively affects the concentration of atmospheric particles. However, the LCZ analysis found that areas with high-density buildings have a positive effect on atmospheric particles, while most areas with low-density buildings significantly reduced the number of atmospheric particles present. Generally, compared with the traditional LUCC landscape classification method, LCZ integrates Shanghai's physical structure and classifies the urban landscape more accurately, which is closely related to the urban atmospheric particulate matter, especially in the urban area. Because the low-density building area has the same effect on the particulate matter as the natural landscape, the use of low-density buildings is recommended when planning new towns.

Synergistic interactions of fine particles and radiative effects in modulating urban heat islands during winter haze event in a cold megacity of Northeast China

Severe air pollution and urban heat islands (UHI) intensity (UHII) are two challenging problems that have attracted wide attention in populated cities. However, previous studies mostly focused on the relationship between fine particulate matter (PM_2.5) and UHII, but how UHII responds to the interactions between radiative effects (direct effect (DE), indirect effect (IDE) with slope and shading effects (SSE)) and PM_2.5 during heavy pollution is still unclear, especially in the cold region. Therefore, this study explores the synergistic interactions between PM_2.5 and radiative effects in influencing UHII during a heavy pollution event in the cold-megacity of Harbin-China. Hence, we designed four scenarios: non-aerosol radiative feedback (NARF), DE, IDE, and combined effects (DE + IDE + SSE) in December 2018 (clear-episode) and December 2019 (heavy-haze-episode) using numerical modeling. The results showed that the radiative effects influenced the spatial distribution of PM_2.5 concentration leading to a mean drop in 2-m air-temperature by approximately 0.67 °C (downtown) and 1.48 °C (satellite-town) between the episodes. The diurnal-temporal variations revealed that the daytime and nighttime UHIIs were strengthened in the downtown during the heavy-haze-episode, while a reverse effect was observed in the satellite-town. Interestingly, during the heavy-haze-episode, the considerable difference between excellent and heavily polluted PM_2.5 levels showed a decrease in UHIIs (1.32 °C, 1.32 °C, 1.27 °C, and 1.20 °C) due to the radiative effects (NARF, DE, IDE, and (DE + IDE + SSE)), respectively. In assessing other pollutants' interactions with the radiative effects, PM₁₀ and NOx had a considerable impact on the UHII during the heavy-haze episode while O₃ and SO₂ were discovered to be very low in both episodes. Moreover, the SSE has uniquely influenced UHII, especially during the heavy-haze-episode. Therefore, insight from this study provides an understanding of how UHII responds uniquely in the cold region, which in turn could help to formulate effective policies and co-mitigation strategies for air pollution and UHI problems.

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO₂, CO, and O₃ in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied (P < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO₂ gas was reduced significantly (P < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O₃ gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.

Urban ventilation corridors and spatiotemporal divergence patterns of urban heat island intensity: a local climate zone perspective

Urban ventilation corridors introduce fresh air into urban interiors and improve urban livability, while mitigating the urban heat island (UHI) effect. However, few studies have assessed the impact of urban ventilation corridors on UHI intensity (UHII) from the perspective of the local climates of different cities. Therefore, this study integrated multisource data to construct ventilation corridors from the perspective of local climate zone (LCZ) and analyzed its impact on UHII. The results showed the following: (1) the average UHII of constructed LCZs was higher than that of natural LCZs, among which the building type LCZ10 (heavy industry) had the highest intensity (5.77 °C); (2) in extracted ventilation corridors, the pixel number of natural LCZs was substantially larger than that of constructed LCZs, among which LCZE (bare soil/paved) was the largest; and (3) for natural LCZs, the average UHII of each LCZ was lower within the ventilated corridors than within the non-ventilated corridors (except for LCZG [water]), with the UHII of LCZB (scattered trees) exhibiting the greatest mitigation effect. Quantitative research on the composition and function of ventilation corridors can not only assess the ability of ventilation corridors to mitigate UHIs, but also provide a reference for urban ventilation corridor planning.

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

Dynamic succession patterns and interactions of phyllospheric microorganisms during NOx exposure

The phyllosphere plays a role in alleviating air pollution, potentially leveraging the native microorganisms for further enhancement. It remains unclear how phyllospheric microorganisms respond to nitrogen oxide (NO_x) pollution and participate in abatement. Here, we exposed Schefflera octophylla to NO_x to reveal microbial succession patterns and interactions in the phyllosphere. During exposure, phyllospheric ammonium (NH₄⁺-N) significantly increased, with different alpha diversity changes between bacteria and fungi. Community successions enclosed core taxa with relatively excellent tolerance, represented by bacterial genera (Norcardiodes, Aeromicrobium) and fungal genera (Talaromyces, Acremonium). The exposure eliminated specific pathogens (e.g., Zymoseptoria) and benefitted plant growth-promoting populations (e.g., Talaromyces, Exiguobacterium), which might favor plant disease control, improve plant health and thus buffer NO_x pollution. Cooccurrence networks revealed more negative correlations among bacteria and closer linkages among fungi during exposure. Our results also showed a functional shift from the predominance of pathotrophs to saprotrophs. Our study identified microbial successions and interactions during NO_x pollution and thus enlightened prospective taxa and potential roles of phyllospheric microorganisms in NO_x remediation.

Combined Effects of Artificial Surface and Urban Blue-Green Space on Land Surface Temperature in 28 Major Cities in China

The two most common land cover types in urban areas, artificial surface (AS) and urban blue-green space (UBGS), interact with land surface temperature (LST) and exhibit competitive effects, namely, heating and cooling effects. Understanding the variation of these effects along the AS ratio gradient is highly important for the healthy development of cities. In this study, we aimed to find the critical point of the joint competitive effects of UBGS and AS on LST, and to explore the variability in different climate zones and cities at different development levels. An urban land cover map and LST distribution map were produced using Sentinel-2 images and Landsat-8 LST data, respectively, covering 28 major cities in China. On this basis, the characteristics of water, vegetation, and LST in these cities were analyzed. Moreover, the UBGS (water or vegetation)–AS–LST relationship of each city was quantitatively explored. The results showed that UBGS and AS have a competitive relationship and jointly affect LST; this competition has a critical point (threshold). When the proportion of UBGS exceeds this value, UBGS replaces AS as the dominant variable for LST, bringing about a cooling effect. In contrast, when AS dominates LST, it causes a warming effect. The critical points between AS and water and between AS and vegetation in 28 major cities in China were 80% and 70%, respectively. The critical point showed an obvious zonal difference. Compared with cities in subtropical and temperate climate regions, the critical point of arid cities is higher, and UBGS exhibited better performance at alleviating the urban thermal environment. The critical point of cities with higher development levels is lower than that of cities with lower development levels. Even areas with relatively low AS coverage are prone to high temperatures, and more attention should be paid to improving the coverage of UBGS. Our research results provide a reference for the more reasonable handling of the relationship between urban construction, landscape layout, and temperature control.

Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.

Thin-layered MoS2 nanoflakes vertically grown on SnO2 nanotubes as highly effective room-temperature NO2 gas sensor

The unique properties of heterostructure materials make them become a promising candidate for high-performance room-temperature (RT) NO₂ sensing. Herein, a p-n heterojunction consisting of two-dimensional (2D) MoS₂ nanoflakes vertically grown on one-dimensional (1D) SnO₂ nanotubes (NTs) was fabricated via electrospinning and subsequent hydrothermal route. The sulfur edge active sites are fully exposed in the MoS₂@SnO₂ heterostructure due to the vertically oriented thin-layered morphology features. Moreover, the interface of p-n heterojunction provides an electronic transfer channel from SnO₂ to MoS₂, which enables MoS₂ act as the generous electron donor involved in NO₂ gas senor detection. As a result, the optimized MoS₂@SnO₂-2 heterostructure presents an impressive sensitivity and selectivity for NO₂ gas detection at RT. The response value is 34.67 (R_a/R_g) to 100 ppm, which is 26.5 times to that of pure SnO_2. It also exhibits a fast response and recovery time (2.2 s, 10.54 s), as well as a low detection limit (10 ppb) and as long as 20 weeks of stability. This simple fabrication of high-performance sensing materials may facilitate the large-scale production of RT NO₂ gas sensors.

Empirical evidence on the impact of urban overheating on building cooling and heating energy consumption

A primary contributor to urban overheating is the urban heat island (UHI) formed due to increased urbanization. The adverse effects of UHI on building energy use are substantial and well documented. However, such effects are typically demonstrated through numerical simulations which are susceptible to modeling uncertainties and lack of validation resulting in a pressing research gap. Here, for the first time, we conduct a large-scale assessment to demonstrate the devastating impact of UHI on building energy consumption using real building energy use data. We find empirical evidence correlating UHI with building energy use; changes in average UHI intensity of 0.5 K correspond to an increase in monthly cooling energy consumption in a range of 0.17 kWh/m²–1.84 kWh/m². The study validates theoretical evidence on the impact of UHI on building energy and proposes a highly innovative methodology to assess the impact of overheating on the energy balance of cities.

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Real energy data used to study the effect of local climates on building energy use.

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Cooling energy rises between 0.17 and 1.84 kWh/m² due to 0.5 K urban overheating.

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Heating energy drops between 0.06 and 1.19 kWh/m² due to 0.5 K urban overheating.

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Urban overheating effects are more exacerbated during the cooling period.

Impacts of Composition and Canopy Characteristics of Plant Communities on Microclimate and Airborne Particles in Beijing, China

As the basic component of urban green-spaces, plant communities regulate both the microclimate and air particle levels. Understanding the regulatory mechanism of plant communities represents the theoretical basis for using green spaces to improve the urban climate and mitigate air particle pollution. Based on field investigations, differences in the daily air temperatures (AT), relative humidity (RH), and PM10 and PM2.5 concentrations in eight compositional types of plant communities were quantitatively analyzed. In addition, the correlations between these variables and various canopy parameters were further established in order to detect critical thresholds. The results showed that, among the eight compositional types, significant differences existed in daily AT, RH, PM10 and PM2.5 levels. The mixed tree, shrub and grass (M-TSG) community had the strongest cooling and PM10 reduction effects; the broad-leafed tree, shrub and grass (B-TSG) community had the best humidifying effect; while the mixed tree and grass (M-TG) community most effectively reduced PM2.5 concentrations. The daily AT and PM10 concentrations were significantly negatively correlated with canopy density (CD) and leaf area index (LAI), but positively correlated with canopy porosity (CP) and sky view factor (SVF), while these correlations were opposite for daily RH. The response of daily PM2.5 concentrations to canopy characteristics was complex, featuring multiple non-linear relations. Critical thresholds were found in some cases. Overall, M-TSG or M-TG communities with about 75% CD, 55% CP, 2.5 LAI and 0.18 SVF perform most noticeable both microclimate and air particle regulation services.

Urban Aerosol Particulate Matter Promotes Necrosis and Autophagy via Reactive Oxygen Species-Mediated Cellular Disorders that are Accompanied by Cell Cycle Arrest in Retinal Pigment Epithelial Cells

Urban particulate matter (UPM) is recognized as a grave public health problem worldwide. Although a few studies have linked UPM to ocular surface diseases, few studies have reported on retinal dysfunction. Thus, the aim of the present study was to evaluate the influence of UPM on the retina and identify the main mechanism of UPM toxicity. In this study, we found that UPM significantly induced cytotoxicity with morphological changes in ARPE-19 human retinal pigment epithelial (RPE) cells and increased necrosis and autophagy but not apoptosis. Furthermore, UPM significantly increased G2/M arrest and simultaneously induced alterations in cell cycle regulators. In addition, DNA damage and mitochondrial dysfunction were remarkably enhanced by UPM. However, the pretreatment with the potent reactive oxygen species (ROS) scavenger N-acetyl-L-cysteine (NAC) effectively suppressed UPM-mediated cytotoxicity, necrosis, autophagy, and cell cycle arrest. Moreover, NAC markedly restored UPM-induced DNA damage and mitochondrial dysfunction. Meanwhile, UPM increased the expression of mitophagy-regulated proteins, but NAC had no effect on mitophagy. Taken together, although further studies are needed to identify the role of mitophagy in UPM-induced RPE injury, the present study provides the first evidence that ROS-mediated cellular damage through necrosis and autophagy is one of the mechanisms of UPM-induced retinal disorders.

Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics

The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.