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In recent years, the surge in air-conditioning ownership and usage has led to significant heat rejection, impacting the surrounding atmosphere. Despite this, studies examining the spatiotemporal effects of air-conditioning heat rejection at a block scale remain limited. Additionally, comparative studies on the role of building areas with air-conditioning systems versus natural underlying surfaces in the urban thermal environment are relatively scarce. This study employs field measurements and ArcGIS technology to investigate the local thermal and humidity environments, as well as the spatiotemporal distribution of heat rejection from air-conditioning systems in Wuyi Square, Changsha. Results show that cooling tower exhausts in commercial buildings maintain relative humidity levels of 95.2% to 99.8% during the day, enhancing surrounding humidity. At night, the humidity aligns with atmospheric levels (from 50.3% to 62.5%). The cooling tower exhaust temperature is approximately 2.2 °C lower during the day and 2.4 °C higher at night compared to the surrounding temperatures. In contrast, exhausts from split-type air-conditioning units in residential buildings have an average relative humidity about 14.2% lower than the atmosphere humidity, with temperature averages being 5.2 °C higher during the day and 6.5 °C higher at night, raising surrounding temperatures. The study also finds that natural surface areas are up to 3.1 °C cooler and 9.6% more humid compared to built environment surfaces. Furthermore, residential areas have air temperatures about 0.3 °C higher than commercial zones, with a humidity distribution approximately 0.5% lower. These findings offer a theoretical foundation for enhancing urban thermal environments and informing urban planning and design. [ABSTRACT FROM AUTHOR]

Background: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO₂) in Hong Kong using a deep learning (DL) structured model. Methods: We collected data from mobile air quality sensors on buses and crowd-sourced Google real-time traffic status as a proxy for real-time traffic emissions. Our DL model was compared with existing machine learning models to assess performance improvements. Using an interpretable machine learning method, we hierarchically evaluated the global, local, and interaction effects for different features. Results: Our DL model outperformed existing machine learning models, achieving R2 values of 0.72 for NO and 0.69 for NO₂. The incorporation of traffic status as a key predictor improved model performance by 9% to 17%. The interpretable machine learning method revealed the importance of traffic-related features and their pairwise interactions. Conclusion: The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.

This paper proposes a simultaneous localisation and mapping (SLAM) framework that uses a handheld hemispherical view LiDAR‐IMU integration system. Inspired by the specific characteristic of the hemispherical view LiDAR, a ground segmentation module based on seed points is designed. The ground points are then downsampled to eliminate redundant vertical constraints. The IMU data and the pre‐processed point cloud are used to perform state estimation via a tightly coupled iterative extended Kalman filter (iEKF) to obtain the pose estimation. The automatically detected loop closures provide closed‐loop constraints for the odometry, and a factor graph ensures the global consistency of the map. Data from diverse scenes are collected via a prototype system. Both qualitative and quantitative experiments are carried out to verify the framework's performance. According to the experimental results, our framework achieves low‐drift, high‐coverage and real‐time performance, outperforming the state‐of‐the‐art LiDAR SLAM methods in our handheld hemispherical view LiDAR‐IMU test sites. For the research community's benefit, the dataset is publicly provided for other researchers to compare against. [ABSTRACT FROM AUTHOR]

The temporal and spatial variations in nitrogen dioxide (NO 2) fluxes in a city are vital for understanding the magnitude of emissions, their attributes, and the dynamics of air pollutants. We measured NO 2 and carbon dioxide (CO 2) fluxes concurrently using the eddy covariance method at 112 m above the ground from 2021 to 2023 in the city center of Sakai, Japan. The mean half-hourly NO 2 flux was 13.3 ± 11.9 (±1 standard deviation; SD) nmol m−2 s−1 from January to December 2021–2023. The diurnal pattern of the NO 2 flux exhibited a daytime peak, and the daytime NO 2 flux was 1.8 times higher on weekdays than on weekends, regardless of the season. Based on flux footprint analysis, the NO 2 fluxes were higher on the western side (20–24 nmol m−2 s−1 on average) than on the eastern side (6–10 nmol m−2 s−1 on average). To identify the main emission source areas at a high resolution, we further obtained the NO 2 , NO, and CO 2 concentrations in each street, including major roads leading to industrial areas and nonmajor roads in residential areas, via mobile measurements with a bicycle or vehicle within the flux footprint from October 2022 to August 2023. Notably, the NO 2 emission hotspots were major roads leading to the city center and waterfront industrial areas where the traffic volume of heavy-duty trucks mostly comprised diesel-powered vehicles. In addition, we examined the ratio of the NO 2 and CO 2 fluxes (FNO 2 /FCO 2), which will be beneficial for identifying the characteristics of NO 2 because CO 2 is co-emitted as vehicle exhaust. The observed flux ratio during day (approximately 0.77 nmol μmol−1) was rescaled to 1.35–1.54 nmol μmol−1 for vehicle origin when contributions of vehicles to CO 2 fluxes and contributions of nitric monoxide (NO) fluxes to total NO x (=NO 2 + NO) fluxes were approximated. The rescaled ratio was close to the ratio of NO x and CO 2 concentrations observed in the tunnel where photochemical reactions can be neglected. Compared to those in other cities, the NO 2 fluxes in Sakai were lower, possibly due to the lower prevalence of diesel vehicles. Measured NO 2 fluxes are useful for modeling atmospheric pollutants, validating emission inventories, and assessing emission reductions under the implementation of policies issued by local governments. • NO 2 fluxes from a city center, Sakai, Japan, were smaller than those from European cities. • The majority of the NO x emissions was most likely associated with road traffic sources. • The lower NO 2 fluxes in Sakai were explained by the lower prevalence of diesel vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Climatologie is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches. [ABSTRACT FROM AUTHOR]

The impact of airports on ambient air pollution is a major concern due to its impact on public health. This study analyzes the sub-micron total particle number concentration (PNC) as a proxy for ultrafine particles in the immediate vicinity of Berlin-Tegel Airport (TXL) based on a mobile measurement campaign in summer 2019. With predominantly westerly winds, 45 measurement runs took place along a 20–30 km route to the east of the airport. The highlights of the study are as follows: 1. Berlin-Tegel Airport had a distinct but a spatially limited impact on the residential areas to the east of the airport. 2. Particle number concentrations in the lee of the airport are significantly higher than the mean of the entire area. 3. Locations along the eastward extension of the runways are significantly more affected than those outside the approach corridor. 4. The impact of airport operations on PNC in the adjacent neighborhood is comparable to the combined impact of busy roads in the area. The closure of Berlin-Tegel Airport at the end of 2020 should have considerably improved the air quality in the residential areas in the close vicinity of the airport.

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately quantifying the concentration and transport flux of atmospheric fine particulate matter (PM 2.5) is vital when attempting to thoroughly identify the pollution formation mechanism. In this study, the mobile lidar measurements in Beijing on heavily polluted days in December from 2015 to 2018 are presented. The lidar was mounted on a vehicle, which could perform measurements along designated routes. On the basis of mobile lidar measurements along closed circuits of the 6th Ring Road around Beijing, the spatial distribution and transport flux of PM 2.5 in Beijing were determined with information of wind field. In the spatial distribution, both the concentration and transport of PM 2.5 were revealed to be more significant in the southern section of Beijing. The regional transport layer at heights < 1.3 km plays an important role in pollution formation. The maximum transport flux reached 1600 μg/(m2*sec) on 11 December 2016. With the aerosol boundary layer height determined from the image edge detection (IED) method, the inter-annual variations of the aerosol boundary layer height (ABLH) were also analysed. The ABLH decreased from 0.73 to 0.46 km during the same heavy pollution period from 2015 to 2018. Increasingly adverse aerosol boundary layer (ABL) meteorological factors, including lower ABLH, light winds, temperature inversions, and accumulated moisture, have become necessary for pollution formation in Beijing. [Display omitted] [ABSTRACT FROM AUTHOR]

PM2.5 concentrations in urban areas are highly variable, both spatially and seasonally. To assess these patterns and the underlying sources, we conducted PM2.5 exposure measurements at the adult breath level (1.6 m) along three ~5 km routes in urban districts of Mainz (Germany) using portable low-cost Alphasense OPC-N3 sensors. The survey took place on five consecutive days including four runs each day (38 in total) in September 2020 and March 2021. While the between-sensor accuracy was tested to be good (R² = 0.98), the recorded PM2.5 values underestimated the official measurement station data by up to 25 µg/m3. The collected data showed no consistent PM2.5 hotspots between September and March. Whereas during the fall, the pedestrian and park areas appeared as hotspots in >60% of the runs, construction sites and a bridge with high traffic intensity stuck out in spring. We considered PM2.5/PM10 ratios to assign anthropogenic emission sources with high apportionment of PM2.5 in PM10 (>0.6), except for the parks (0.24) where fine particles likely originated from unpaved surfaces. The spatial PM2.5 apportionment in PM10 increased from September (0.56) to March (0.76) because of a pronounced cooler thermal inversion accumulating fine particles near ground. Our results showed that highly resolved low-cost measurements can help to identify PM2.5 hotspots and be used to differentiate types of particle sources via PM2.5/PM10 ratios. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization throughout the world, climate problems related to climate change including urban heat islands and global warming have become challenges to urban human settlements. Numerous studies have shown that greenways are beneficial to urban climate improvement and can provide leisure places for people. Taking the coastal greenway in Qingdao as the research object, mobile measurements of the microclimate of the greenway were conducted in order to put forward an evaluation method for the research of outdoor thermal comfort. The results showed that different vegetation coverage affected the PET (physiologically equivalent temperature), UTCI (Universal Thermal Climate Index) as well as thermal comfort voting. We found no significant correlation between activities, age, gender, and thermal comfort voting. Air temperature sensation and solar radiation sensation were the primary factors affecting the thermal comfort voting of all sections. Otherwise, within some sections, wind sensation and humidity sensation were correlated with thermal sensation voting and thermal comfort voting, respectively. Both PET and UTCI were found to have a negative correlation with the vegetation coverage on both sides of the greenway. However, the vegetation coverage had positive correlation (R = 0.072) for thermal sensation and significant positive correlation (R = 0.077*) for thermal comfort. The paved area cover was found to have a positive correlation with PET and UTCI, while having a negative correlation with thermal sensation (R = −0.049) and thermal comfort (R = −0.041). This study can provide scientific recommendations for the planning and design of greenway landscapes to improve thermal comfort. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH _3 ) plays a significant role in the nitrogen cycle, and can have impacts on air quality, ecological balance and climate change. While NH _3 associated with natural and agricultural processes has long been considered the primary source, the contribution of combustion-related NH _3 , particularly from vehicular emissions, keeps on the rise. We found that high on-road NH _3 concentrations occurred in a metropolitan city based on mobile measurement, and inferred that urban vehicular NH _3 emission was likely underestimated in the past. NH _3 emission factors (EFs) were derived from ring roads and tunnels, showing levels 74% and 20% higher than the latest standard proposed by Euro VII, respectively. To quantify the underestimation, two methods based on car ownership and traffic flow were used to estimate the annual vehicular NH _3 emission in Shanghai as 2.59 and 1.76 Gg, respectively, substantially surpassing the predicted results by the Dynamic Projection model for Emissions in China. Given these discoveries, we recommend that it is urgent and imperative to establish relevant national standards and limits aiming at regulation on vehicular NH _3 emissions. And more representative EFs measurements should be adopted to improve the accuracy of inventory estimation.

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches.

With the acceleration of urbanization throughout the world, climate problems related to climate change including urban heat islands and global warming have become challenges to urban human settlements. Numerous studies have shown that greenways are beneficial to urban climate improvement and can provide leisure places for people. Taking the coastal greenway in Qingdao as the research object, mobile measurements of the microclimate of the greenway were conducted in order to put forward an evaluation method for the research of outdoor thermal comfort. The results showed that different vegetation coverage affected the PET (physiologically equivalent temperature), UTCI (Universal Thermal Climate Index) as well as thermal comfort voting. We found no significant correlation between activities, age, gender, and thermal comfort voting. Air temperature sensation and solar radiation sensation were the primary factors affecting the thermal comfort voting of all sections. Otherwise, within some sections, wind sensation and humidity sensation were correlated with thermal sensation voting and thermal comfort voting, respectively. Both PET and UTCI were found to have a negative correlation with the vegetation coverage on both sides of the greenway. However, the vegetation coverage had positive correlation (R = 0.072) for thermal sensation and significant positive correlation (R = 0.077*) for thermal comfort. The paved area cover was found to have a positive correlation with PET and UTCI, while having a negative correlation with thermal sensation (R = −0.049) and thermal comfort (R = −0.041). This study can provide scientific recommendations for the planning and design of greenway landscapes to improve thermal comfort.

Many types of living plants release gaseous trimethylamine (TMA), making it a potentially important contributor to new particle formation (NPF) in remote areas. However, a panoramic view of the importance of forest biogenic TMA at the regional scale is lacking. Here, we pioneered nationwide mobile measurements of TMA across a transect of contiguous farmland in eastern China and a transect of subtropical forests in southern China. In contrast to the farmland route, TMA concentrations measured during the subtropical forest route correlated significantly with isoprene, suggesting potential TMA emissions from leaves. Our high time-resolved concentrations obtained from a weak photo-oxidizing atmosphere reflected freshly emitted TMA, indicating the highest emission intensity from irrigated dryland (set as the baseline of 10), followed by paddy field (7.1), subtropical evergreen forests (5.9), and subtropical broadleaf and mixed forests (4.3). Extrapolating their proportions roughly to China, subtropical forests alone, which constitute half of the total forest area, account for nearly 70% of the TMA emissions from the nation's total farmland. Our estimates, despite the uncertainties, take the first step toward large-scale assessment of forest biogenic amines, highlighting the need for observational and modeling studies to consider this hitherto overlooked source of TMA.

Particulate matter (PM) sources differ in urban environments and may show spatiotemporal distinct patterns for varying particle aerodynamic diameters (D P). We here assess such patterns using high-resolution PM ≤10 μm data recorded with a cargo bike along a 14 km route through urban, suburban, and rural areas in Mainz (Germany). The measurements conducted twice a day between May and August 2021 reveal decreasing particle number concentration (PNC) with increasing D P including ∼6000 times higher particle numbers at D P 0.22–0.25 μm compared to D P 4–5 μm. Total mass concentration is bi-modally distributed and dominated by particles <0.3 μm and from 3 to 5 μm representing 36 and 22% of the entire air load, respectively. PM concentrations in Mainz are significantly higher in the morning than in the afternoon, and PM 1 and PM 10 are 13 and 31% higher in urban compared to surrounding suburban and rural areas. The high-resolution measurements also revealed 30% higher PM concentrations at D P 3–5 μm in the urban compared to the rural sectors, which is indicative for road dust, brake and tyre abrasion as the main source. D P distribution in rural hotspots is generally shifted toward larger particles >3 μm, most likely related to natural dust from agricultural fields. These findings show that high-resolution PM profiles can skillfully be recorded using bikes as mobile platform to identify spatial pollution patterns and attribute D P spectra to particular emission sources. • PM 1 and PM 10 are significantly higher in the morning than in the afternoon. • PM 1 and PM 10 are 13 and 31% higher in urban compared to surrounding rural areas. • Particle numbers decrease significantly with increasing diameter along the track. • Total mass is bi-modally distributed and dominated by particles <0.3 μm and 3–5 μm. • Mass diameter distributions indicate traffic as main emission at urban hotspots. [ABSTRACT FROM AUTHOR]

• Determine the repeated areas of the MLS point clouds by detecting the trajectory. The number of points within the neighborhood of each trajectory point is counted once and compared with the neighborhood point density threshold determined by the MLS motion speed to judge whether it is a duplicate point. • A trajectory-based adaptive partitioning of repeated areas' point clouds is proposed. To lessen the effect of non-rigid deformations on registration, MLS point clouds are segmented coarsely where trajectory angle changes occur and finely where speed fluctuations occur. • The approach to employ automatically extracted point cloud features for multi-constraint joint registration is advanced. These scene features, essentially constituted by a variety of basic features such as points, lines, and surfaces, are automatically extracted by the deep learning network to achieve multi-constraint joint registration of MLS point clouds in repeated areas. Urban digitalization, autonomous driving, and 3D map construction all benefit greatly from the ability of mobile LiDAR systems (MLS) to swiftly capture centimeter-level accuracy point cloud data of urban scenes. However, the positional accuracy of MLS point clouds is affected by factors such as GNSS signal loss and IMU inertial drift, which can lead to ghosting and layering of point clouds in repeated areas, reducing its usability. Point cloud registration which was developed to solve the MLS localization problem has been studied for a long time. Most current registration methods only consider the point clouds globally, so the registration of repeated areas has not received adequate attention; and the few methods that target repeated areas fail to balance registration accuracy and automation. To address the above-mentioned issues, we propose an automatic multi-constraint joint registration method for MLS point clouds in repeated areas. Firstly, based on the strict correspondence between trajectory and point clouds, this method identifies point cloud repeated areas by searching for repeated areas in the trajectory. Secondly, it adaptively segments the point clouds of repeated areas based on changes in trajectory angle and speed. Then, it uses a deep learning network to automatically extract the scene features of these segmented point clouds, which are essentially constrained by a variety of basic features such as points, lines, and surfaces. Finally, these multi-constrained scene features are used to perform joint registration of point clouds in repeated areas. We carried out repeated areas registration experiments in a campus scene using our self-developed MLS. The results show that the best registration combination can produce errors of 0.021 m and 0.033 m, respectively, compared with pre-registration errors of 0.094 m and 0.103 m. Our method specifically considers MLS point clouds in repeated areas and ensures satisfactory accuracy while achieving automatic point cloud registration. It is also expected that the proposed method can be extended to other LiDAR platforms that also have trajectories, such as Airborne Laser Scanning (ALS) and Simultaneous Localization and Mapping (SLAM), to realize high-precision automatic registration of point clouds in repeated areas, with little adaptation to the complex scenes. [ABSTRACT FROM AUTHOR]

Exposure to PM 2.5 (particles with an aerodynamic diameter equal to or less than 2.5 μm) is associated with a variety of negative health outcomes. Measurements from sparsely situated air quality monitoring stations (AQMSs) may be inappropriate to represent real PM 2.5 exposures, particularly in traffic-related environments. In this study, efforts were made to characterize spatiotemporal variation of PM 2.5 pollution over Shenzhen, China from July 2019 to June 2020 using combined mobile (on-road PM 2.5) and stationary (AQMS PM 2.5) measurements. Monthly-average concentrations of on-road PM 2.5 ranged from 10.4 ± 6.1 to 47.3 ± 23.9 μg/m3, and showed consistent trend with AQMS PM 2.5 concentrations which ranged from 8.3 ± 3.1 to 37.2 ± 12.9 μg/m3. On-road PM 2.5 and AQMS PM 2.5 concentrations dropped by 54.6% and 30.2% in February 2020, probably due to the low anthropogenic emissions during the period of Spring Festival and COVID-19 lockdown. Weekend effect on both on-road and AQMS PM 2.5 concentrations was not noticeable. Relative high on-road PM 2.5 concentrations were observed during morning and evening rush hours. An "elevated concentration" concept was applied to estimate the influence of emissions on PM 2.5 exposure. Elevated concentration showed strong diurnal and spatial variation, and was about 5.0 μg/m3 on-average. Mappings of on-road PM 2.5 and elevated concentrations confirmed the heterogeneity of spatial distribution of PM 2.5 exposures in Shenzhen where PM 2.5 pollution was more severe in western and northern areas. Our results highlight the elevated PM 2.5 exposures in traffic-related environments, and the inequity in urban exposure levels and health. • Temporal effect on on-road PM 2.5 concentrations was investigated. • Spatial variability of on-road PM 2.5 concentrations over a megacity was characterized. • A city-scale assessment of on-road PM 2.5 concentrations above AQMSs was performed. [ABSTRACT FROM AUTHOR]

Tunnels are the preferred experimental environments for estimating vehicle emission factors (EFs) under real-world driving conditions. In this study, online measurements of traffic-related air pollutants (including CO 2 , NO X , SO 2 , O 3 , particulate matter [PM], and volatile organic compounds [VOCs]) were conducted using a mobile laboratory in the Sujungsan Tunnel in Busan, Korea. Mobile measurements generated concentration profiles of the target exhaust emissions inside the tunnel. These data were used to produce a zonation of the tunnel, i.e., mixing and accumulation zones. There were differences between the CO 2 , SO 2 , and NO X profiles, and a starting point that was free from ambient air mixing effects could be set at 600 m from the tunnel entrance. The EFs of vehicle exhaust emissions were calculated using pollutant concentration gradients. The average EFs for CO 2 , NO, NO 2 , SO 2 , PM 10 , PM 2.5 , and ∑VOCs were 149,000, 380, 55, 29.2, 9.64, 4.33, and 16.7 mg km−1·veh−1, respectively. Among the VOC groups, alkanes contributed more than 70% of the VOC EF. Mobile measurement-derived EFs were validated using the conventional EFs from stationary measurements. The EF results from the mobile measurements matched those from the stationary measurements, while the absolute concentration differences between them implied complex aerodynamic movements of the target pollutants inside the tunnel. This study demonstrated the usefulness and advantages of applying mobile measurements in a tunnel environment and indicated the potential of the approach for observation-based policymaking. [Display omitted] • Tunnel mobile measurement was conducted to estimate the emission factors (EFs) of vehicle exhausts. • The concentration gradient approach was applied, and demonstrated reliabilities for the estimation of EFs. • High time-resolution data enabled zonation of the tunnel as mixing and accumulation zone. • Region-specific pollutants of concern were updated by comparison with national inventory data. [ABSTRACT FROM AUTHOR]

Urban populations face increasing heat stress in cities. However, the influence of the built environment of new and old urban blocks on pedestrian thermal comfort remains unclear. This study selected typical old (Yongqingfang) and new urban areas (Knowledge City) in Guangzhou, China, as our research sites. Through field monitoring and surveys, we used physiological equivalent temperature (PET) and thermal comfort vote (TCV) to evaluate outdoor thermal comfort by thermal walk experiments. We analyzed the relationships between built environment variables, meteorological variables, and pedestrian thermal comfort at the two sites. Our analysis revealed significant differences in the built environment and meteorological conditions between the new and old urban blocks within the 60-m buffer zone. PET and TCV showed noticeable spatiotemporal variations in both sites, and their correlation was stronger in the morning (r = 0.87–0.89) than late afternoon (r = 0.60–0.70). Our stepwise regression model indicated that sky view factor and building coverage ratio significantly affected outdoor thermal comfort in old and new urban blocks. Built environment variables explained a higher percentage of the variance in PET (Yongqingfang R2: 0.59–0.82, Knowledge City R2: 0.32–0.81) than TCV (Yongqingfang R2: 0.45–0.57, Knowledge City R2: 0.48–0.69). In short, built environment variables affected thermal indices more than thermal perception. The impact of built environment variables on TCV is also greater in new urban areas than in old urban blocks. Our findings provide insights into the complex relationship between built environments and outdoor thermal comfort in different urban landscapes, which informs climate-resilient urban design. • PET and TCV in different urban blocks were obtained by thermal walks. • Urban morphology explained a higher variance in new urban blocks' TCV than old blocks. • Solar angle and building shade affect the diurnal variation in thermal comfort. • Built environment variables affect PET more than TCV. • SVF and building density play important roles in the impact of thermal comfort. [ABSTRACT FROM AUTHOR]

Urban green spaces play a crucial role in regulating the micro-climates of their surrounding built-up areas, which is particularly important in high-density cities. A field-measurement method with high accuracy and convenient movement is always adopted to assess the micro-climates and regulating capacity. Compared with fixed observation, mobile measurements have been found to offer advantages such as the need for lesser manpower and instruments. Despite the role of mobile measurements in characterizing urban thermal environments, there has still been a lack of consistent methods, leading to measurement errors. Accordingly, this study aimed to assess the applicability of mobile-measurement strategies by a two-day field campaign around People's Park in Guangzhou, China. Numerous findings were noted. Mobile measurements on the precinct scale required a staying time of at least 6 min to account for the response time of the probes. Additionally, the most applicable time periods for mobile measurements were the afternoon and even the evening. In terms of thermal characterization, the optimized reentry-transect method should be adopted for periods with abrupt changes in the ambient temperatures. Lastly, the block park has been observed to exert a more substantial 'cool island' effect under the colder weather on both days. Its park cooling intensity (PCI) in the late summer ranged from 0.55 to 1.82 °C, with a cooling efficiency of 0.33 °C/100 m, while that in the early autumn ranged from 0.49 to 3.18 °C, with a cooling efficiency of 0.50 °C/100 m. ● Staying time of mobile measurements at the local scale should be more than 6 min. ● A stable ambient air temperature (T a) is priority for mobile measurements. ● Mobile-measurement results need to be calibrated when T a changes precipitously. ● Block park shows a better cooling intensity under a low T a during the measurement. [ABSTRACT FROM AUTHOR]