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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.

The axial Mach number distribution of the core flow for model in a transonic wind tunnel is an important index to evaluate the performance of the flow field, which is usually measured by the centerline probe. In order to simulate the incoming flow characteristics without interference, the probe will extend from the support section to the shrinkage section, so the probe usually must has longer inches, more static pressure measuring points and smaller blockage requirements. In order to study the influence of the points of the centerline probe on the uniformity distribution of flow field, a new static pressure probe is designed, which is smaller and shorter than the centerline probe. On the basis of the stability of the flow field, the Mach number distribution of the flow field measured by the static pressure probe which is driven by the moving measuring mechanism. The characteristics of the measured values are studied by wind tunnel test. The results show that: when Ma ≤ 0.95, the overall distribution and value of Mach number obtained by the static pressure probe is basically the same as those obtained by the centerline probe, but some flow field details, which mainly shows that Mach number of the static pressure probe has smaller fluctuation, higher accuracy and better uniformity index.

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.

It is evident that the rapid development in science and technology has enabled various electronic devices and communication systems. However, there are also undesired effects of those devices on human health. One of those effects is the electromagnetic fields that exceeds the limits. Therefore, the electromagnetic fields emitted by the devices should not exceed a certain level. In that respect, different institutions around the world have determined the certain limits. One of the most important of these organizations in the world is the International Commission on Non-Ionizing Radiation Protection (ICNIRP). Similar to the other existing organizations, the Information and Communication Technologies Authority (BTK) is the responsible authority in Turkey. In this study, electric field intensity values were measured using the drive test method with the Selective Radiation Meter (SRM-3006) device on the main streets in Diyarbakır City Center. Obtained measurement values were shown on dot thematic maps, two-dimensional maps and threedimensional maps. As a result, locations with the highest electric field intensity were determined by these maps. In addition, the obtained results were compared with the limit values determined by ICNIRP and BTK and it was seen that the measured electric field intensity values did not exceed the exposure limits. [ABSTRACT FROM AUTHOR]

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.

Previous studies have indicated that temperature regulation is related to social behavior (for an overview, see IJzerman et al., 2015; IJzerman & Hogerzeil, 2017). However, precise causal relationships between temperature and social behaviors are unclear. These links may be better understood by frequently measuring temperature in daily life and mapping those measurements onto social behaviors. The primary purpose of the present study was to enable such studies by validating a new wireless temperature sensor, the ISP131001 from Insight SiP, for human peripheral temperature measurement in daily life. In our exploratory dataset, we found moderately high correlations between two ISP131001 sensors and a comparison sensor ('r' = .82 for the average of our two ISP sensors). These correlations replicated in our confirmatory dataset ('r' = .94 for the average of our two ISP sensors). A secondary purpose of this report is the inclusion of a standard set of relevant measures for social thermoregulation research. We believe that this standard protocol of measures be included in all future social thermoregulation studies in order to facilitate and encourage data re-use and aggregation across studies.

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.

The portable microAeth® MA200 (MA200) is widely applied for measuring black carbon (BC) in human exposure characterization and mobile air quality monitoring. However, the field lacks information about this instrument's performance under various settings. This study evaluated the real-time performance of the MA200 in an urban area, Augsburg, Germany. Noise reduction and negative value mitigation were explored using different data processing methods: local polynomial regression (LPR), optimized noise reduction averaging (ONA), and centered moving average (CMA) under different interval time (5s, 10s, and 30s). After noise reduction, the data were evaluated and compared by (1) the relative number of negative values; (2) more detailed microenvironmental change information retained after noise reduction; (3) the reduction of the peak values and number of peak samples; (4) more detailed microenvironmental change retained after the background correction. Our results showed that CMA showed a good prospect to analyze the raw BC concentration data in terms of the interval time due to its proportions of negative values and the detail microenvironmental change. Moreover, the CMA method has the highest reduction peak values and the number of peak samples compared to ONA and LPR. Furthermore, after background correction, the CMA treatment results remained more detailed microenvironmental changes in pollutants than others. Therefore, based on a comprehensive comparison, CMA offered a good approach to post-process the raw BC concentration data. These findings provide new insight for the noise reduction approach that applied in mobile monitoring campaign using BC instruments. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that temperature regulation is related to social behavior (for an overview, see IJzerman et al., 2015; IJzerman & Hogerzeil, 2017). However, precise causal relationships between temperature and social behaviors are unclear. These links may be better understood by frequently measuring temperature in daily life and mapping those measurements onto social behaviors. The primary purpose of the present study was to enable such studies by validating a new wireless temperature sensor, the ISP131001 from Insight SiP, for human peripheral temperature measurement in daily life. In our exploratory dataset, we found moderately high correlations between two ISP131001 sensors and a comparison sensor (r = .82 for the average of our two ISP sensors). These correlations replicated in our confirmatory dataset (r = .94 for the average of our two ISP sensors). A secondary purpose of this report is the inclusion of a standard set of relevant measures for social thermoregulation research. We believe that this standard protocol of measures be included in all future social thermoregulation studies in order to facilitate and encourage data re-use and aggregation across studies. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology 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.)

In this study we focus on a detailed analysis of air temperature in the city centre of Olomouc, using a bicycle for mobile measurements. We studied the Spatial pattern of air temperature, analysed temperature differences between local climate zones (LCZs) and identifed hot spots and cool spots in the city centre. The results point to a signifcant influence of microclimate and local climate on the field of temperature. In the daytime, hotspots occurred namely in LCZ 8 and E and in the well irradiated spaces within LCZ 2. Larger areas of scattered trees (LCZ B) in combination with watercourses created cool spots with a cooling potential for their surroundings. During night time, the warmest spots were detected mostly in LCZ 2 and the coolest spots in areas with low plants (LCZ D).

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.

Summer extreme high-temperatures occur frequently in large cities; urban spatial form is the primary factor affecting the urban thermal environment. Thus, planning and arranging urban spaces is a key approach to regulating urban microclimates. Studies into how urban spatial forms influence the formation of urban microclimates have been carried out for multiple cities in warm and hot regions; however, few studies of this kind have been carried out for cities in cold regions. In this study, we analyze Zhengzhou, a city located in a cold region of China, using summer 2017 measurement data to determine why high temperatures develop in cold areas. We investigated how temperature and humidity vary during the morning, at noon, and in the evening given different land use properties (commercial and residential) and different spatial forms (building height, building density, green coverage rate, and plot ratio); we then studied the correlation between urban spatial form and the urban thermal environment. Our research results indicate that the commercial district’s thermal microclimate was related to PR and BH in the afternoon and GCR in the morning and at night. In the residential district, the key urban morphology factors related to its thermal microclimates were BD, PR, and GCR during almost the whole day.

This paper measures the temperatures along a test route through Chenggong District, Kunming, by mobile measurement, and revises the measured data to 4 different time points of the test period, aiming to enhance the data utilization efficiency. The modified data were plotted into isotherm slices at the four time points, and the urban heat island (UHI) features, as well as its influencing factors were analysed on geographic information system (GIS) software. The results of the measurements showed that, high temperatures were measured in university district, residential area and government administrative area are the highest, reaching 19.94°C, 20.01°C, and 20.05°C, respectively, while the lowest temperature was observed at Guanshan Reservoir, which was only 17.64°C. The maximum UHI intensity stood at 2.41°C, which occurred at 19:30. In addition, cooling effect of local water bodies depends on the size of the water body and the distance from the living area. [ABSTRACT FROM AUTHOR]

Abstract Black carbon (BC) has emerged as a major contributor to global climate change. Cities play an important role in global BC emission. The present study investigated the spatial pattern of in-traffic BC at a high spatial resolution in Shanghai, the commercial and financial center in Mainland China. The determinants including road network, social economic status and point-source pollutants, which may influence the BC spatial variability were also discussed. From October to December 2016, mobile monitoring was conducted to assess the BC concentrations on three sampling routes in Shanghai with a total length of 116 km. The results showed that the mean in-traffic BC among three sampling routes was 10.77 ± 3.50 μg/m3. BC concentrations showed a significant spatial heterogeneity. The highest BC concentrations were near industrial sources and that those high concentrations were associated with either direct emissions from the industries, freight traffic, or both. With the widely distributed polluting enterprises and high emitting vehicles, the average BC in the low urbanization areas (12.80 ± 4.54 μg/m3) was 57% higher than that in the urban core (7.77 ± 2.24 μg/m3). Furthermore, a land use regression (LUR) model based on mobile monitoring was developed to examine the determinants and its spatial variability of BC measurements which corresponded to 17 predictor variables, e.g. road network, land use, meteorological condition etc., in 7 buffer distances (100 m to 10 km). The variables of meteorological, socio-economical and the distance to BC point-sources were selected as the independent variables. It was found that the established LUR model could explain a proportion (68%) of the variability of BC. LUR modeling from mobile measurements was possible, but more work related to the effect of traffic regulation on BC could be helpful for informing best model practice. Graphical abstract Unlabelled Image Highlights • Mobile monitoring of in-traffic black carbon (BC) was conducted in Shanghai, China. • BC was lowest in the urban core and increased towards the outer areas of the city. • Land use regression (LUR) model could explain 68% of the BC spatial variability. • The results implied the effect of traffic policy on the spatial BC in a Mega city. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a quantitative determination of the NO x emission in Hefei using mobile Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements. The measurements were carried out during winter time from December 2016 to February 2017. The measured NO 2 vertical column densities (VCDs) show a strong spatial gradient with higher values within the city center, indicating the majority of NO x emission sources are located at the city center. A significant "holiday effect" was found by comparing measurements taken during weekdays and weekend. The weekend reduction is more significant in the suburban (∼28%) compared to the city center (∼13%). Mobile measurements of NO 2 vertical column densities (VCDs) were analyzed together with meteorological data to determine the NO x emission by applying the loop-integral method. Detailed error analysis of the NO x emission shows the variation of wind field and large measurement gap dominated the total error of NO x emission calculation. The result shows the NO x emission in Hefei during winter time varies in a wide range from 10 × 1024 to 40 × 1024molecs−1 with an average of 18.44 × 1024molecs−1. Our estimation is about 43% lower than the number reported in the previous emission inventory in 2012. The reduction of NO x emission reflects the successful implementation of emission control measures in recent years. Our result also shows about 73% of the total NO x in the city were transported from outside of the city during winter. The impacts of transported NO x are especially large when air masses originated from heavily polluted regions, i.e. North China Plain and Yangtze River Delta. The result presented provides a better understanding of the impacts of local emissions and transportation of pollutants on the local air quality of Hefei. Highlights • NO x emission in Hefei was determined using mobile MAX-DOAS measurements in 2017. • NO x emission in Hefei varies from 10 to 40 × 1024 molec/s during winter. • Our estimation is ∼43% lower compared to previous emission inventory in 2012. • About 73% of NO x measured in the city were transported from outside. [ABSTRACT FROM AUTHOR]

Traditional precision measurement adopts discrete artificial static observation, which cannot meet the demands of the dynamic, continuous, fine and high-precision holographic measurement of large-scale infrastructure construction and complex operation and maintenance management. Due to its advantages of fast, accurate and convenient measurement, mobile laser scanning technology is becoming a popular technology in the maintenance and measurement of infrastructure construction such as tunnels. However, in some environments without satellite signals, such as indoor areas and underground spaces, it is difficult to obtain 3D data by means of mobile measurement technology. This paper proposes a method to restore the linear of the point cloud obtained by mobile laser scanning based on the measured track center line. In this paper, the measured track position is interpolated with a cubic spline to calculate the translations, and the rotation parameters are calculated by combining the simulation design data. The point cloud of the cross-section of the tunnel under the local coordinate system is converted to the absolute coordinate system to calculate the tunnel line. In addition, the method is verified by experiments combined with the subway tunnel data, and the overall point error can be controlled to within 0.1 m. The average deviation in the horizontal direction is 0.0551 m, and that in the vertical direction is 0.0274 m. Compared with the previous methods, this method can effectively avoid the obvious deformation of the tunnel and the sharp increase in the error, and can process the tunnel point cloud data more accurately and quickly. It also provides better data support for subsequent tunnel analysis such as 3D display, completion survey, systematic hazard management and so on.

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]

Ultrafine particles (UFP, diameter < 100 nm) exposure has already been associated with adverse effects on human health. Spatial distribution of UFP is non-uniform; they concentrate in the vicinity of the source, e.g. traffic, because of their short lifespan. This work investigates spatial distribution of UFP in three areas in the Czech Republic with different traffic load: High traffic (Prague neighborhood—Sporilov), commuter road vicinity (Libeznice), and a small city with only local traffic (Celakovice). Size-resolved measurements of particles in the 5–500 nm range were taken with a particle classifier mounted, along with batteries, GPS and other accessories, on a handcart and pushed around the areas, making one-minute or longer stops at places of interest. Concentrations along main roads were elevated in comparison with places farther from the road; this pattern was observed in all sites, while particle number distributions both close and away from main roads had similar patterns. The absence of larger particles, the relative absence of higher concentrations of particles away from the main roads, and similar number distributions suggest that high particle number concentrations cannot be readily attributed to sources other than internal combustion engines in vehicles and mobile machinery (i.e., mowers and construction machines).

In this study we focus on a detailed analysis of air temperature in the city centre of Olomouc, using a bicycle for mobile measurements. We studied the spatial pattern of air temperature, analysed temperature differences between local climate zones (LCZs) and identified hot spots and cool spots in the city centre. The results point to a significant influence of microclimate and local climate on the field of temperature. In the daytime, hotspots occurred namely in LCZ 8 and E and in the well irradiated spaces within LCZ 2. Larger areas of scattered trees (LCZ B) in combination with watercourses created cool spots with a cooling potential for their surroundings. During night time, the warmest spots were detected mostly in LCZ 2 and the coolest spots in areas with low plants (LCZ D). [ABSTRACT FROM AUTHOR]

People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R 2 ) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) has been identified as a threat to urban living quality in the context of climate change. As awareness of the impacts of urban expansion on local climate increases, urban planners/decision makers attempt to incorporate climatic considerations into the planning process. An increasingly-used urban climatic analysis scheme— Local Climate Zone (LCZ) classification— has been applied in Hong Kong, a high-density city with heterogeneous an urban environment. This study aims to evaluate the LCZ mapping in such a unique urban context using in-situ air temperature data. The fine-scale spatial variation of the daytime and nighttime screen-level air temperatures was investigated via mobile measurements during the summertime of 2016. The measured data were collated in Geographic Information System (GIS) based on the current LCZ maps. Statistically significant air temperature differences were observed between most LCZ classes, which confirm the veracity of LCZ in high-density heterogeneous urban contexts. Higher uncertainties in the site-averaged air temperature and considerable intra-LCZ air temperature differences in LCZs 1 to 6 were observed. It indicates that the current LCZ procedures of Hong Kong can be further refined for a better understanding of the climatic heterogeneity in densely built urban areas. [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]

Cities have multiple fugitive emission sources of methane (CH4) and policies adopted by China on replacing coal with natural gas in recent years can cause fine spatial heterogeneities at the range of kilometers within a city and also contribute to the CH4 inventory. In this study, a mobile observatory was used to monitor the real-time CH4 concentrations at fine spatial and temporal resolutions in Beijing, the most important pilot city of energy transition. Results showed that: several point sources, such as a liquefied natural gas (LNG) power plant which has not been included in the Chinese national greenhouse gas inventory yet, can be identified; the ratio “fingerprints” (CH4:CO2) for an LNG carrier, LNG filling station, and LNG power plant show a shape of “L”; for city observations, the distribution of CH4 concentration, in the range of 1940−2370 ppbv, had small variations while that in the rural area had a much higher concentration gradient; significant correlations between CO2 and CH4 concentrations were found in the rural area but in the urban area there were no such significant correlations; a shape of “L” of CH4:CO2 ratios is obtained in the urban area in wintertime and it is assigned to fugitive emissions from LNG sources. This mobile measurement methodology is capable of monitoring point and non-point CH4 sources in Beijing and the observation results could improve the CH4 inventory and inform relevant policy-making on emission reduction in China.

This study is based on International Electrotechnical Commission (IEC) 61967‐4 standards for measuring electromagnetic emissions and interferences at the chip level. Conventional measurement methods using fixed SMA connectors were replaced with an improved radio frequency current probe featuring semi‐rigid coaxial cables, making it capable of taking mobile measurements, enhancing the flexibility and convenient probing usage. To effectively assess the practical value of the improved probe, the authors have referred to the standard designs provided in IEC 61967‐1 to manufacture a four‐layer digital circuit board that controls the screen of an on‐board navigational screen for automobiles. Circuit board electromagnetic interference (EMI) noise frequency and level readings from the improved probe were also compared with three other measurement methods, namely magnetic field probes available in the market, TEM cells, and the 10‐m far‐field measurement method. The results demonstrated the capability of the improved probe to effectively identify sources of noise at the chip‐level during the IC design phases, helping with root cause analysis and allowing EMI noises experienced by the product to be solved promptly during the process of electromagnetic compatibility design optimisation and analysis. [ABSTRACT FROM AUTHOR]

Across a complex urban region with diversified urban patterns, the local climate usually varies with time and space. Achieving accurate local climatic characteristics by using efficient field survey method contributes to providing theoretical support and technical reference for climate-conscious eco-city construction. This study takes the Shenzhen Overseas Chinese Town (OCT) as research area, and mainly focuses on the local-scale urban heat island (UHI) characteristics under different urban patterns. An efficient method by integrating both the mobile measurement and GIS-based spatial interpolation is proposed and applied. By applying temporal corrections to the obtained mobile data, the local UHI intensities (LUHII) along the mobile route are expressed. Then based on the LUHII values along the mobile route, three main spatial interpolation methods (SIMs) are respectively applied to obtain the LUHII spatial distributions within the whole OCT. The spatial distribution results illustrate that the LUHII within the OCT express obvious spatial-temporal characteristics. Further verifying the results of SIMs with 24 field measurement data, the three SIMs present total average mean absolute error (MAE) and root mean square error (RMSE) values of about 0.3 °C. Then quantitative effects of three urban pattern indicators on LUHII are discussed and two equations for expressing the LUHII in two seasons demonstrate that decreasing the building density could help relieve local-scale UHI effects. [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]

Mobile temperature and humidity measurements have been performed along a 17-km transect in a local urban area in Shenzhen (China), during the period from August to December in 2013. The spatial-temporal distributions of Urban Heat Island intensity (UHII) show that the overall average UHII at different times vary between 0 o C and 2 o C. The daily average UHII change from 0 o C to 3 o C. The spatial distributions of UHII express obvious UHII differences as a whole. The correlation coefficients between spatial influential factors and the UHII reveal that the formation of UHII is influenced by both the urban surface characteristics and weather conditions. [ABSTRACT FROM AUTHOR]

Biomedical applications generally needs measurement the human body parts in motion. On the other hand, the analysis of the human motion includes mobile measurements. The mobile measurement is complicated task because it needs two or more sensor combination, specific measurement techniques and huge computation. Thus, it is actual research topic in photogrammetry and computer sciences community. Time-of-flight (ToF) camera can make measurement the moving object. It can be used for robotic and simultaneous localization and mapping applications. Human motion capture is recent application area for ToF camera. In this study analysis of the body motion were made with time-of-flight camera. We made measurement to runner on treadmill. The motion was analysed with computing the angle between body parts. [ABSTRACT FROM AUTHOR]