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Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO2 and PM2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O3. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO2 and PM2.5 changed along with the seasonal variation. Local contributions for PM2.5 changed slightly; however, NO2 showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0–40.6% and 20.8–42.2% of the total concentration for NO2 and PM2.5. The highly polluted days account for 56.3% of total NO2, highlighting local traffic is the dominant contributor to short-term NO2 concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of "hot" spots for PM2.5 and NO2 on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM2.5 and NO2 pollution in urban areas and emphasize the urgent need for mitigating NO2 from traffic pollution in Dublin. [ABSTRACT FROM AUTHOR]

Field-based tibial acceleration measurements are increasingly common but sampling frequencies vary between accelerometers. The purpose of this study was to determine the minimum sampling frequency needed for reliable and accurate measurement of peak axial and resultant tibial acceleration during running in the field. Tibial acceleration was measured at 7161 Hz in 19 healthy runners on concrete and grass. Acceleration data were down sampled to approximate previously used sampling frequencies. Peak axial and resultant tibial acceleration were calculated for each sampling frequency. The within-session reliability and accuracy of peak axial and resultant tibial accelerations were evaluated using intraclass correlation coefficients, mean differences, and 95% limits of agreements. Intraclass correlation coefficients greater than.9 indicated excellent within-session reliability for both peak axial and resultant tibial acceleration measured while running on concrete and grass. Peak axial and resultant tibial accelerations were 0.5 to 1.4 g lower and minimal detectable differences were up to 0.6 g higher at 102 Hz compared with higher sampling frequencies. We recommend a minimum sampling frequency of 199 Hz for accurate and reliable measurements of peak axial and resultant tibial acceleration in the field. [ABSTRACT FROM AUTHOR]

Mobile measurement techniques are increasingly utilized to monitor urban emissions, regional air quality, and air pollutant exposure assessments. This study employed a bicycle measurement method to obtain the detailed distribution of air pollutant concentrations in roadside, commercial, residential, and recreational areas. The study area is located in Chuncheon, South Korea, with approximately 280,000 residents. Black carbon (BC), PM2.5, and NO2 were monitored using portable devices equipped on an electric bicycle. Results showed that in the evening (6–8 p.m.), the concentrations were higher in both commercial and residential areas compared to the background location, while concentrations were notably elevated only in roadside areas in the morning (8–10 a.m.). Spatial mapping of measured concentrations revealed that the highest concentrations corresponded to areas with densely operated charbroiling meat-cooking restaurants. Additionally, it was confirmed that BC and PM2.5 emitted from the commercial areas influenced nearby recreational areas (e.g., streamside roads). In conclusion, this study demonstrated that air pollutant hotspots resulting from human activities, such as dining at commercial restaurants, significantly worsen the local air quality on a small scale. Efforts to reduce the uncontrolled emissions of air pollutants from charbroiling meat-cooking restaurants are necessary. [ABSTRACT FROM AUTHOR]

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures. [ABSTRACT FROM AUTHOR]

As China's urbanization process accelerates, the issue of air pollution becomes increasingly prominent and urgently requires improvement, based on the fact that environmental conditions such as meteorology and topography are difficult to change. Therefore, relevant optimization studies from the perspective of architectural patterns are operable to mitigate pollution. This paper takes the Wenhua Road block in Shenyang, China, as the research object; obtains the concentration data of three kinds of particulate matter through fixed and mobile monitoring; and analyzes the spatial distribution characteristics of Local Climate Zones (LCZ) and particulate matter in the block based on the ArcGIS platform, identifies high-risk areas, and excavates the influence of LCZ on the concentrations of three kinds of particulate matter. The results show that the spatial distribution characteristics of PM1, PM2.5, and PM10 under the same pollution level are relatively similar, while the spatial heterogeneity of the distribution of the same particulate matter under different pollution levels is higher. The time-weighted results show that the PM1 pollution level in the block ranges from 44 to 51 μg/m³, PM2.5 ranges from 75 to 86 μg/m³, and PM10 ranges from 87 to 99 μg/m³. The pollution hot spots throughout the year are located in the central, eastern and western parts of the study area. In terms of the relationship between the LCZ and particulate matter, with the increase in the particulate matter diameter, the correlation between the three kinds of particulate matter and LCZ are all enhanced. The built-up LCZ always has a larger average concentration of particulate matter than that of the natural LCZ, and building height and building density are the main factors causing the difference. In the optimal design of the risk area, the proportion of natural vegetation or water surface should be increased and the building height should be properly controlled and the building density should be reduced in the renewal of the urban building form. This study will largely improve the spatial refinement of the optimization of urban architectural patterns oriented to mitigate particulate matter pollution. [ABSTRACT FROM AUTHOR]

The widespread use of fossil fuels in daily commuting vehicles poses a significant health risk to urban populations. This study aims to quantify the escalation of black carbon (BC) emissions from traffic and assess their environmental impact. BC monitoring was conducted using a motorcycle to estimate mobile concentrations emitted by vehicles along two distinct routes during three phases (M-period, A-period, and E-period). The average mobile BC concentration (± SD) was found to be 32.57 ± 22.78 μg m-3, with the highest average value observed during the E-period at 34.1 μg m-3 for route-1. In route-2, BC concentration was 29.12 ± 21.08 μg m-3, with the mean highest at 33.4 during the E-period. The likelihood of increased BC emissions is evident when individuals venture out to markets in the evening. Weekend BC concentrations (25.76 μg m-3) were generally lower than weekdays (39.38 μg m-3) during the analysis of route-1. A similar trend was observed in route-2, attributed to reduced traffic volume and vehicular emissions resulting from the closure of schools and offices on weekends. Throughout the study, BC levels ranged from 10.91 to 149 μg m-3 in TR-1 and 9.76 to 114 μg m-3 in TR-2. Notably, at five intersection points on TR-1, BC concentrations at T102 were 34% higher during evening times than at T105. Similarly, among the four intersection points on TR-2, BC levels at T201 were 55% higher than T203 during the evening. Our analysis also showed that inhalation doses (IDOSE) were highest during peak traffic times, with motorcyclist IDOSE ranging from 15.29 to 25.01 µg. This study highlights the concerning levels of BC exposure during daily commuting, emphasizing the need for measures to mitigate the health impacts associated with urban traffic emissions and promote a safer environment for urban populations. [ABSTRACT FROM AUTHOR]

Oil refineries and petrochemical facilities contribute to the pollution of the environment either air, water, or soil releasing hazardous and toxic air pollutants into the atmosphere. These include nitrogen oxides (NOx), carbon monoxide (CO), hydrogen sulfide (H2S), sulfur dioxide (SO2), and particulate matter (PM), affecting both environment and human health. In the range of air pollutants, hydrogen sulfide is less monitored and reported despite its importance in determining adverse effects. Background H2S concentrations in air were found to range commonly from 0.11 ppb to 0.33 ppb. The concentrations in urban areas can reach 1 ppb (~1.4 g m-3) up to more than 90 ppb (~125 g m-3) near sources of H2S emissions. In Romania, the standard for the air in protected areas for the hydrogen sulfide pollutant provides an admissible concentration, over a maximum interval of 30 minutes, of 15 µg/m³. Everything that exceeds this value is considered harmful to human health. The statistics for the H2S time series recorded in Ploiești City by the mobile laboratory of the Environmental Protection Agency Prahova between 2019 and 2021 with a sampling rate of 30 minutes showed that the mean ranged between 1.51 and 3.74 µg/m³, while the maximum reached 59.36 µg/m³ in the east of Ploiești in 2019. Overall, the highest variance occurred in the East and North parts due to the presence of the concentration peaks. Hydrogen sulfide must be considered on the public health agenda, both from the occupational perspective and for the ambient concentrations near significant H2S sources and in background urban areas. The monitoring infrastructure needs to be improved to allow continuous surveillance to ensure reliable databases that support research regarding the health effects of H2S across the concentrations occurring in the urban environment.

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent. [ABSTRACT FROM AUTHOR]

Introduction: Exposure to Particulate Matter (PM) can cause ill health effects such as coughing, allergies, decreased lung function, chest discomfort and pain. The current study aims to monitor particulate matter concentrations on the highways in Nashik, India and to estimate its exposure to the bikers in the form of Respiratory Deposition Doses (RDDs) with its seasonal variation. Materials and methods: Low-cost air quality monitor was mounted on the bike to measure Particulate Matter (PM1, PM2.5 and PM10) concentrations at breathing level. Extensive mobile monitoring was performed on seven highway stretches passing through city limits at morning and evening peak hours for all the weekdays for three seasons. Results: The PM concentrations differed on each route seasonally as well as at peak hours in morning and evening. The maximum PM1, PM2.5 and PM10 concentrations monitored were 119.84 μg/m3 , 218.85 μg/m3 and 239.25 μg/m3 respectively on Route R3 in Winter morning. The maximum RDDHD exposure on R5 and R3 in Winter mornings was due to PM10. While maximum RDDTB and RDDAL exposure on R5 in Winter morning and evening was due to PM2.5. Also, the seasonal and particle size effect on RDD has been studied which exhibits higher rise in exposure in Winter mornings due to PM2.5. Conclusion: This study reveals that maximum exposure was observed during Winter mornings. The results recommend that seasons have a substantial effect on PM concentrations and their exposure. The minimum exposure was observed in monsoon, followed by summer and the maximum exposure was seen in winters.

Mobile measurement techniques are increasingly utilized to monitor urban emissions, regional air quality, and air pollutant exposure assessments. This study employed a bicycle measurement method to obtain the detailed distribution of air pollutant concentrations in roadside, commercial, residential, and recreational areas. The study area is located in Chuncheon, South Korea, with approximately 280,000 residents. Black carbon (BC), PM2.5, and NO2 were monitored using portable devices equipped on an electric bicycle. Results showed that in the evening (6–8 p.m.), the concentrations were higher in both commercial and residential areas compared to the background location, while concentrations were notably elevated only in roadside areas in the morning (8–10 a.m.). Spatial mapping of measured concentrations revealed that the highest concentrations corresponded to areas with densely operated charbroiling meat-cooking restaurants. Additionally, it was confirmed that BC and PM2.5 emitted from the commercial areas influenced nearby recreational areas (e.g., streamside roads). In conclusion, this study demonstrated that air pollutant hotspots resulting from human activities, such as dining at commercial restaurants, significantly worsen the local air quality on a small scale. Efforts to reduce the uncontrolled emissions of air pollutants from charbroiling meat-cooking restaurants are necessary.

As China’s urbanization process accelerates, the issue of air pollution becomes increasingly prominent and urgently requires improvement, based on the fact that environmental conditions such as meteorology and topography are difficult to change. Therefore, relevant optimization studies from the perspective of architectural patterns are operable to mitigate pollution. This paper takes the Wenhua Road block in Shenyang, China, as the research object; obtains the concentration data of three kinds of particulate matter through fixed and mobile monitoring; and analyzes the spatial distribution characteristics of Local Climate Zones ( LCZ) and particulate matter in the block based on the ArcGIS platform, identifies high-risk areas, and excavates the influence of LCZ on the concentrations of three kinds of particulate matter. The results show that the spatial distribution characteristics of PM1, PM2.5, and PM10 under the same pollution level are relatively similar, while the spatial heterogeneity of the distribution of the same particulate matter under different pollution levels is higher. The time-weighted results show that the PM1 pollution level in the block ranges from 44 to 51 μg/m³, PM2.5 ranges from 75 to 86 μg/m³, and PM10 ranges from 87 to 99 μg/m³. The pollution hot spots throughout the year are located in the central, eastern and western parts of the study area. In terms of the relationship between the LCZ and particulate matter, with the increase in the particulate matter diameter, the correlation between the three kinds of particulate matter and LCZ are all enhanced. The built-up LCZ always has a larger average concentration of particulate matter than that of the natural LCZ, and building height and building density are the main factors causing the difference. In the optimal design of the risk area, the proportion of natural vegetation or water surface should be increased and the building height should be properly controlled and the building density should be reduced in the renewal of the urban building form. This study will largely improve the spatial refinement of the optimization of urban architectural patterns oriented to mitigate particulate matter pollution.

Mobile air pollution measurements have the potential to provide a wide range of insights into emission sources and air pollution exposure. The analysis of mobile data is, however, highly challenging. In this work we develop a new regression-based framework for the analysis of mobile data with the aim of improving the potential to draw inferences from such measurements. A quantile regression approach is adopted to provide new insight into the distribution of NOx and CO emissions in Central and Outer London. We quantify the emissions intensity of NOx and CO (ΔNOx/ΔCO2 and ΔCO/ΔCO2) at different quantile levels (τ) to demonstrate how transient high-emission events can be examined in parallel to the average emission characteristics. We observed a clear difference in the emissions behaviour between both locations. On average, the median (τ = 0.5) ΔNOx/ΔCO2 in Central London was 2x higher than Outer London, despite the stringent emission standards imposed throughout the Ultra Low Emissions Zone. A comprehensive vehicle emission remote sensing data set (n ≈ 700,000) is used to put the results into context, providing evidence of vehicle behaviour which is indicative of poorly controlled emissions, equivalent to high-emitting classes of older vehicles. Our analysis suggests the coupling of a diesel-dominated fleet with persistently congested conditions, under which the operation of emissions after-treatment technology is non-optimal, leads to increased NOx emissions.

The most significant effect of industrialization is air pollution. Air pollution monitoring has become an important requirement in affected areas. With the advancement of electronic sensor technology and the internet of things (IoT), there are plenty of remote ambient air quality monitoring systems have been developed by measuring the levels of major air pollutants such as Sulfur Dioxide, (SO2), Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3), and particulate matter (PM). However, other hazardous gases such as Hydrogen Sulfide (H2S), Carbon Monoxide (CO), and Ammonia (NH3) also have the potential to appear in factory areas, especially factory areas located in rural areas. Therefore, this study proposes a low-cost mobile ambient air quality monitoring system by measuring CO, H2S, and NH3 gas levels that are easy to implement. The proposed system has been tested in factory areas and rural areas where factory pollutants can be mixed with agricultural pollutants. The test results show that the proposed system can detect CO, H2S, and NH3 gas levels. The levels of CO, H2S, and NH3 gases at the test site were still at a safe level. A mobile remote monitoring scheme by utilizing Thinger.io as an IoT platform also works very well. [ABSTRACT FROM AUTHOR]

It is necessary to predict the spatial variation in particle number concentration (PNC) and black carbon (BC) because they are considered air pollutants associated with traffic and many diseases. In this study, land use regression (LUR) models for PNC and BC were developed based on a mobile monitoring campaign in January 2020 in Lanzhou, and the performance of models was evaluated with hold-out validation (HV) and leave-one-out cross-validation (LOOCV). The results show that the adjusted R2 of the LUR models for PNC and BC are 0.51 and 0.53, respectively. The R2 of HV and LOOCV are 0.43 and 0.44, respectively, for the PNC model and 0.42 and 0.50, respectively, for the BC model. The performances of the LUR models are of a moderate level. The spatial distribution of the predicted PNC is related to the distance from water bodies. The high PNC is related to industrial pollution. The BC concentration decreases from south to north. High BC concentrations are associated with freight distribution centres and coal-fired power plants. The range of PNC particle sizes in this study is larger than in most studies. As one of few studies in Lanzhou to develop LUR models of air pollutants, it is important to accurately estimate pollutant concentrations to improve air quality and provide health benefits for residents. [ABSTRACT FROM AUTHOR]

The ongoing improvement of urban air quality urgently needs refined understanding of air pollution variation. For urban roads, due to the changeable traffic flow and complex road environments, commuters usually confront with a direct but uncertain exposure to traffic-induced air pollutants. However, the current lack of fine-grained measurements and reliable analytical methods restricts our knowledge of road air pollution risk. Therefore, we designed a bicycling experiment to collect fine-scale concentration samples of PM2.5, PM10, and black carbon (BC) in non-motorized lanes beside an expressway. Mobile measurements revealed high particle pollution at the building-intensive roadside, and the background pollution concentration removal clarified high-polluted sections. Generalized additive models demonstrated that the background pollution concentrations dominated the overall pattern of particles, and meteorological factors had significant but varied impacts on local variations of particles. Riverside winds lowered PM2.5 and PM10 levels most time, while BC was more affected by roadside greenery, distance from roadway and diesel vehicles. At the hotspots, an increase of 100 diesel vehicles per hour could increase roadside BC by about 2% per kilometer but brought no obvious increase in PM2.5 and PM10. These results confirm the availability of mobile measurements and generalized additive models in high-resolution pollution analysis, and are beneficial to countermeasures of reducing personal exposure risk of slow-moving traffic. [ABSTRACT FROM AUTHOR]

Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel. [ABSTRACT FROM AUTHOR]

Municipal solid waste landfills are major contributors to anthropogenic emissions of methane (CH4), which is the major component of natural gas, a potent greenhouse gas, and a precursor for the formation of tropospheric ozone. The development of sensitive, selective, and fast-response instrumentation allows the deployment of mobile measurement platforms for CH4 measurements at landfills. The objectives of this study are to use mobile monitoring to measure ambient levels of CH4 at eight large operating landfills in southeast Michigan, USA; to characterize diurnal, daily and spatial variation in CH4 levels; and to demonstrate the influence of meteorological factors. Elevated CH4 levels were typically found along the downwind side or corner of the landfill. Levels peaked in the morning, reaching 38 ppm, and dropped to near-baseline levels during midday. Repeat visits showed that concentrations were highly variable. Some variation was attributable to the landfill size, but both mechanistically-based dilution-type models and multivariate models identified that wind speed, boundary layer height, barometric pressure changes, and landfill temperature were key determinants of CH4 levels. Collectively, these four factors explained most (r2 = 0.89) of the variation in the maximum CH4 levels at the landfill visited most frequently. The study demonstrates the ability to assess spatial and temporal variation in CH4 levels at landfills using mobile monitoring along perimeter roads. Such monitoring can identify the location of leaks and the best locations for long-term emission monitoring using fixed site monitors. [ABSTRACT FROM AUTHOR]

The distinction between satellite-based land surface temperature (LST) and air temperature has become an increasingly important part of managing urban heat islands. While the preponderance of urban heat research relies on LST, the emergence of a growing infrastructure of publicly available consumer oriented, ground-based sensor networks has offered an alternative for characterizing microscale differences in temperatures. Recent evidence suggests large differences between LST and air temperatures, yet discerning the reason for these differences between satellite-derived measurements of urban heat islands (UHI) and ground-based measurements of air temperature remains largely unresolved. In this study, we draw on an unusually robust and spatially exhaustive dataset of air temperature in two distinct bioclimates—Portland, Oregon, USA and Washington, D.C., USA—to evaluate the role of land cover on temperature. Our findings suggest that LST in highly built environments is consistently higher than recorded air temperatures, at times varying upwards of 15-degree Celsius, while forested areas contain between 2.5 and 3.5-degree Celsius lower temperatures than LST would otherwise indicate. Furthermore, our analyses points to the effects of land use and land cover features and other geophysical processes may have in determining differences in heat measurements across the two locations. The strength of the present analyses also highlights the importance of hyperlocal scales of data used in conjunction with coarser grain satellite derived data to inform urban heat assessments. Our results suggest a consistent pattern in both study areas, which can further our capacity to develop predictive models of air temperature using freely available descriptions of LST. [ABSTRACT FROM AUTHOR]

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Measurements of tibial acceleration during running must be reliable to ensure valid results and reduce errors. The purpose of this study was to determine the reliability and minimal detectable difference (MDD) of peak axial and peak resultant tibial acceleration during overground and treadmill running. The authors also compared reliability and MDDs when peak tibial accelerations were determined by averaging 5 or 10 trials. Tibial acceleration was measured during overground and treadmill running of 19 participants using a lightweight accelerometer mounted to the tibia. Peak axial and peak resultant tibial accelerations were determined for each trial. Intraclass correlation coefficients determined within-session reliability, and MDDs were also calculated. Within-session reliability was excellent for all conditions (intraclass correlation coefficients =.95–.99). The MDDs ranged from 0.6 to 1.4 g for peak axial acceleration and from 1.6 to 2.0 g for peak resultant acceleration and were lowest for peak axial tibial acceleration during overground running. Averaging 10 trials did not improve reliability compared to averaging 5 trials but did result in small reductions in MDDs. For peak axial tibial acceleration only, lower MDDs indicate that overground running may be the better option for detecting small differences. [ABSTRACT FROM AUTHOR]

The development of research in the field of creating a mobile monitoring system for diagnosing the technical condition of mining equipment in terms of mechanical vibration parameters is one of the most science-intensive areas. The paper presents the results of investigations on the possibility of isolating the mechanical vibrations of the handles with a natural frequency of 5 Hz, which occur with a greater or lesser amplitude in almost all registered spectra, arising from breakages of cutters on the cutting discs of double-flow drives of the executive body of a mining machine. Therefore, it can be used for operational mobile monitoring of breakages in the cutters of the executive bodies of mining machines. An analysis of vibrations has confirmed the fact that they are transmitted to almost all non-rotating parts of the combine, where they can be registered, which allows them to be used for mobile automated monitoring of cutter breakages on cutting discs. On the example of the combine “Universal-600” it has been found that the most convenient when registering these vibrations from the standpoint of the absence of the need to introduce a signal transmission module from the rotating parts of the combine into the measuring path can be considered the placement of a vibration sensor on the body of the input gearbox of the executive body drive with its orientation in the direction combine movements, where oscillations with a frequency of 5 Hz are prevalent and it is quite simple to isolate them from the low-frequency range using standardized low-frequency one-third octave filters. The paper proposes a technique for creating a system for automated mobile monitoring of cutter breakages based on the parameters of mechanical vibrations of cutting discs with natural frequency.

Ozone (O 3) is a secondary atmospheric pollutant that can adversely impact human health, with production rates dependent on chemical precursors such as nitrogen oxides (NOx), Volatile Organic Compounds (VOCs), as well as meteorological variables such as incoming solar radiation, temperature, and humidity. NO x concentrations in Salt Lake City, UT (SLC), have decreased significantly since 2000 (−60%). However, during this time minimal declines in O 3 were observed. To better understand the drivers behind O 3 trends in SLC, we leveraged air quality measurements from sites maintained by the Utah Division of Air Quality, along with mobile O 3 measurements collected on top of a light-rail train that traverses the Salt Lake Valley (SLV). Analyses were carried out from March 15th through April (spring) and June through August (summer) for 2019, 2020, and 2021 and focused on the afternoon when O 3 was typically at the highest concentrations (1–4 p.m. MST). Overall, there were large differences in O 3 between the spring and summer months, with the latter being higher by ∼10 ppb. However, there were relatively small differences in O 3 between weekday and weekend. For the spring, differences in afternoon O 3 concentrations were higher during weekends (<12%), while during the summer, the differences were smaller (1–2%) and higher during weekdays. While weekday vs weekend differences in NO x emissions are usually large (∼20%) due to changes in traffic activity, there were relatively minimal differences in O 3 between weekday and weekends. There were minimal differences at point locations such as downtown SLC, and along multiple points along the TRAX train line. The analyses carried out in this study suggests that cities such as SLC are primarily under the influence of VOC-limited chemical regimes, meaning that there is limited O 3 sensitivity to changes in NO x. These results suggest that reducing VOC emissions, would be the most effective approach for reducing O 3 pollution in cities like SLC. • Ozone (O 3) levels in Salt Lake City exhibit slight decline despite a significant (−60%) reduction in NO x concentrations since 2000. • Seasonal variations in O 3 concentrations show summer levels are 10 ppb higher than spring, with weekday vs. weekend differences <12%. • Analysis indicates that Salt Lake City is mainly VOC-limited, stressing the need to reduce VOC emissions alongside NOx reductions to mitigate O3 pollution. [ABSTRACT FROM AUTHOR]

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM 2.5), nitrogen dioxide (NO 2), and ozone (O 3) in Quebec City, Canada. We conducted a combined fixed-site (NO 2 and O 3) and mobile monitoring (PM 2.5 , BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am–10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces. Annual models for PM 2.5 , NO 2 , O 3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54–0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM 2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants. [Display omitted] • Fixed-site sampling was used to create annual concentrations of NO 2 and O 3. • Mobile monitoring models for PM 2.5 , BC, BrC, UFP size, and counts for 9 UFP sizes. • Models had high out of sample predictive accuracy (range R2: 0.54–0.86). • Spatiotemporal models were developed for total PM 2.5 , UFP, BC, and BrC. • 24–32 'visits' were required to attain model stability for PM 2.5 , UFP, BC and BrC. [ABSTRACT FROM AUTHOR]

In the current scenario of massive urbanization and global climate change, an intelligent monitoring of the environmental variables is becoming fundamental to ensure good living conditions in cities. Indeed, the acquisition of data with high spatiotemporal resolution can enable the assessment of environmental vulnerabilities in urban areas towards the definition of responsive adaptation and mitigation strategies. In this context, the current work presents a two-fold approach based on low-cost cloud-connected sensors for (i) fixed and (ii) mobile monitoring of several environmental parameters. This paper, which focuses on the measurement aspects of the urban micro-climate, describes in detail the hardware and software components of both approaches, and how to exploit them for setting up a field campaign. The methods were tested in the city of Bolzano (Italy), demonstrating their suitability for identifying the spatial variability of the microclimate in relation to the urban morphology, and for highlighting the presence of the urban heat island and estimating its intensity.

BACKGROUND:Some studies have linked long-term exposure to traffic related air pollutants (TRAP) with adverse cardiovascular health outcomes; however, previous studies have not linked highly variable concentrations of TRAP measured at street-level within neighborhoods to cardiovascular health outcomes. METHODS:Long-term pollutant concentrations for nitrogen dioxide [NO2], nitric oxide [NO], and black carbon [BC] were obtained by street-level mobile monitoring on 30 m road segments and linked to residential addresses of 41,869 adults living in Oakland during 2010 to 2015. We fit Cox proportional hazard models to estimate the relationship between air pollution exposures and time to first cardiovascular event. Secondary analyses examined effect modification by diabetes and age. RESULTS:Long-term pollutant concentrations [mean, (standard deviation; SD)] for NO2, NO and BC were 9.9 ppb (SD 3.8), 4.9 ppb (SD 3.8), and 0.36 μg/m3 (0.17) respectively. A one SD increase in NO2, NO and BC, was associated with a change in risk of a cardiovascular event of 3% (95% confidence interval [CI] -6% to 12%), 3% (95% CI -5% to 12%), and - 1% (95% CI -8% to 7%), respectively. Among the elderly (≥65 yrs), we found an increased risk of a cardiovascular event of 12% for NO2 (95% CI: 2%, 24%), 12% for NO (95% CI: 3%, 22%), and 7% for BC (95% CI: -3%, 17%) per one SD increase. We found no effect modification by diabetes. CONCLUSIONS:Street-level differences in long-term exposure to TRAP were associated with higher risk of cardiovascular events among the elderly, indicating that within-neighborhood differences in TRAP are important to cardiovascular health. Associations among the general population were consistent with results found in previous studies, though not statistically significant.

Industrial emissions of volatile organic compounds (VOCs) directly impact air quality downwind of facilities and contribute to regional ozone and secondary organic aerosol production. Positive matrix factorization (PMF) is often used to apportion VOCs to their respective sources using measurement data collected at fixed sites, for example air quality monitoring stations. Here, we apply PMF analysis to high time-resolution VOC measurement data collected both while stationary and while moving using a mobile monitoring platform. The stationary monitoring periods facilitated the extraction of representative industrial VOC source profiles while the mobile monitoring periods were critical for the spatial identification of VOC hotspots. Data were collected over five days in a heavily industrialized region of southwestern Ontario containing several refineries, petrochemical production facilities and a chemical waste disposal facility. Factors associated with petroleum, chemical waste and rubber production were identified and ambient mixing ratios of selected aromatic, unsaturated and oxygenated VOCs were apportioned to local and background sources. Fugitive emissions of benzene, highly localized and predominantly associated with storage, were found to be the dominant local contributor to ambient benzene mixing ratios measured while mobile. Toluene and substituted aromatics were predominantly associated with refining and traffic, while methyl ethyl ketone was linked to chemical waste handling. The approach described here facilitates the apportionment of VOCs to their respective local industrial sources at high spatial and temporal resolution. This information can be used to identify problematic source locations and to inform VOC emission abatement strategies. [ABSTRACT FROM AUTHOR]

Background: Machine learning methods are proposed to improve the predictions of ambient air pollution, yet few studies have compared ultrafine particles (UFP) models across a broad range of statistical and machine learning approaches, and only one compared spatiotemporal models. Most reported marginal differences between methods. This limits our ability to draw conclusions about the best methods to model ambient UFPs., Objective: To compare the performance and predictions of statistical and machine learning methods used to model spatial and spatiotemporal ambient UFPs., Methods: Daily and annual models were developed from UFP measurements from a year-long mobile monitoring campaign in Quebec City, Canada, combined with 262 geospatial and six meteorological predictors. Various road segment lengths were considered (100/300/500 m) for UFP data aggregation. Four statistical methods included linear, non-linear, and regularized regressions, whereas eight machine learning regressions utilized tree-based, neural networks, support vector, and kernel ridge algorithms. Nested cross-validation was used for model training, hyperparameter tuning and performance evaluation., Results: Mean annual UFP concentrations was 13,335 particles/cm 3 . Machine learning outperformed statistical methods in predicting UFPs. Tree-based methods performed best across temporal scales and segment lengths, with XGBoost producing the overall best performing models (annual R 2  = 0.78-0.86, RMSE = 2163-2169 particles/cm 3 ; daily R 2  = 0.47-0.48, RMSE = 8651-11,422 particles/cm 3 ). With 100 m segments, other annual models performed similarly well, but their prediction surfaces of annual mean UFP concentrations showed signs of overfitting. Spatial aggregation of monitoring data significantly impacted model performance. Longer segments yielded lower RMSE in all daily models and for annual statistical models, but not for annual machine learning models., Conclusions: The use of tree-based methods significantly improved spatiotemporal predictions of UFP concentrations, and to a lesser extent annual concentrations. Segment length and hyperparameter tuning had notable impacts on model performance and should be considered in future studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background. The paper raises the problem of reducing the non-cardiac component of the electrocardiosignal in the recordings of long-term monitoring of the electrical activity of the heart, a study that is currently based on the use of classical methods for filtering discrete signals, which does not allow to fully assess the influence of non-cardiac factors on the shape of the electrocardiogram. Materials and methods. To reduce the noncardiac component of the electrocardiosignal in the recordings of long-term monitoring of the electrical activity of the heart, the author proposes to use adaptive filtering of the electrocardiosignal using a reconstruction filter with a nonlinear transfer characteristic, the parameters of which are determined based on the measurement of the bioimpedance modulus of the patient's thoracic tissues. Results. Based on the results of measuring the bioimpedance modulus, a nonlinear characteristic of the reconstruction filter was obtained, with the help of which the procedure for restoring the shape of the electrocardiogram was carried out and the relative reduced shape recovery error was calculated. Conclusions. The fundamental difference of the approach proposed by the author lies in the application of new methods of nonlinear filtering of discrete signals based on the use of additional information about the nature of the influencing impacts.

Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel.

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The mobile cardiac acoustic monitoring system is a promising tool to enable detection and assist the diagnosis of left ventricular systolic dysfunction (LVSD). The objective of the study was to evaluate the diagnostic value of electromechanical activation time (EMAT), an important cardiac acoustic biomarker, in quantifying LVSD among left bundle branch pacing (LBBP) and right ventricular apical pacing (RVAP) patients using a mobile acoustic cardiography monitoring system. In this prospective single-center observational study, pacemaker-dependent patients were consecutively enrolled. EMAT, the time from the start of the pacing QRS wave to first heart sound (S1) peak; left ventricular systolic time (LVST), the time from S1 peak to S2 peak; and ECG were recorded simultaneously by the mobile cardiac acoustic monitoring system. LVEF was measured by echocardiography. A logistic regression model was applied to evaluate the association between EMAT and reduced EF (LVEF < 50%). A total of 105 pacemaker-dependent patients participated. The RVAP group (n = 58) displayed a significantly higher EMAT than the LBBP group (n = 47) (150.95 ± 19.46 vs. 108.23 ± 12.26 ms, p < 0.001). Pearson correlation analysis revealed a statistically significant negative correlation between EMAT and LVEF (p < 0.001). Survival analysis showed the sensitivity and specificity of detecting LVEF to be < 50% when EMAT ≥ 151 ms were 96.00% and 96.97% in the RVAP group. In LBBP patients, the sensitivity and specificity of using EMAT ≥ 110 ms as the cutoff value for the detection of LVEF < 50% were 75.00% and 100.00%. There was no significant difference in LVST with or without LVSD in the RVAP group (p = 0.823) and LBBP group (p = 0.086). Compared to LVST, EMAT was more helpful to identify LVSD in pacemaker-dependent patients. The cutoff point of EMAT for diagnosing LVEF < 50% differed regarding the pacing type. Therefore, the mobile cardiac acoustic monitoring system can be used to identify the progress of LVSD in pacemaker patients. [ABSTRACT FROM AUTHOR]

Municipal solid waste landfills are major contributors to anthropogenic emissions of methane (CH4), which is the major component of natural gas, a potent greenhouse gas, and a precursor for the formation of tropospheric ozone. The development of sensitive, selective, and fast-response instrumentation allows the deployment of mobile measurement platforms for CH4 measurements at landfills. The objectives of this study are to use mobile monitoring to measure ambient levels of CH4 at eight large operating landfills in southeast Michigan, USA; to characterize diurnal, daily and spatial variation in CH4 levels; and to demonstrate the influence of meteorological factors. Elevated CH4 levels were typically found along the downwind side or corner of the landfill. Levels peaked in the morning, reaching 38 ppm, and dropped to near-baseline levels during midday. Repeat visits showed that concentrations were highly variable. Some variation was attributable to the landfill size, but both mechanistically-based dilution-type models and multivariate models identified that wind speed, boundary layer height, barometric pressure changes, and landfill temperature were key determinants of CH4 levels. Collectively, these four factors explained most (r2 = 0.89) of the variation in the maximum CH4 levels at the landfill visited most frequently. The study demonstrates the ability to assess spatial and temporal variation in CH4 levels at landfills using mobile monitoring along perimeter roads. Such monitoring can identify the location of leaks and the best locations for long-term emission monitoring using fixed site monitors.

Facing the need of modern agriculture to accurately grasp the information of farmland diseases and pests, this paper proposes an intelligent plant protection system. The system is composed of a wireless lens, temperature and humidity sensor, intelligent information terminal, and probe rod to realize the collection of plant images and meteorological information. At the same time, a software based on the mobile terminal and the computer terminal was developed. The plant images and meteorological data are transmitted to the server through Wi-Fi transmission. Combined with the expert knowledge model, a solution is generated, and the user can identify the current diseases and pests and obtain solutions at any time. The system can remotely and automatically monitor and warn of mainstream diseases and pests of field crops such as rice and wheat and provide support for fine plant protection management. [ABSTRACT FROM AUTHOR]

To make the daily monitoring of coal mine safety monitoring system not limited to the PC terminal, the synchronous monitoring and analysis can be carried out anytime and anywhere through the mobile terminal. A mobile monitoring platform based on Android system is established, the real-time display of underground environment information and alarm information realizes by collecting the data of the monitoring system. Through the selection of monitoring points and time periods, timely query and analysis of underground environment change trend, abnormal data statistical analysis are conducted.

The literature on smartphone-based monitoring in psychiatry is expanding. Data collection by utilizing smartphones can be divided into: (a) active monitoring, requiring active input from the patient, and (b) automatically generated passive monitoring. Numerous studies have investigated the potential of both active and passive monitoring in providing insights into mood disorder psychopathology. Furthermore, several intervention studies have been conducted in order to examine the utility of smartphonebased monitoring in the treatment of mood disorders. This literature review aims to provide an overview of the recent studies on using smartphones for patient monitoring among people suffering from mood disorders, and to discuss the current concerns and risks in the field. Smartphones have been demonstrated to be a valid and potentially useful tool for active monitoring of mood disorder patients. However, further research is needed to establish the validity of automatically generated passive data and effectiveness of smartphone-based interventions.

World over there have been isolated cases of reported radiation emergencies, occurring at a very small probability. Environmental radiation mapping of vast area using mobile platforms is one of the methodologies used for quick radiological assessment. The utilization of several portable radiation monitoring instruments for mapping an area and the techniques developed are described. Design aspects of mobile monitoring vehicle for appropriate positioning of detectors and the speed of vehicle are optimized. The mobile radiological monitoring of cities help in quickly generating baseline data and data of a city is given in this paper. The utility of gamma spectrometer in a mobile monitoring vehicle are dealt in detail. The paper discusses various aspects of mobile monitoring, methodology, its application and representation on geographical information system under different situations.

The distinction between satellite-based land surface temperature (LST) and air temperature has become an increasingly important part of managing urban heat islands. While the preponderance of urban heat research relies on LST, the emergence of a growing infrastructure of publicly available consumer oriented, ground-based sensor networks has offered an alternative for characterizing microscale differences in temperatures. Recent evidence suggests large differences between LST and air temperatures, yet discerning the reason for these differences between satellite-derived measurements of urban heat islands (UHI) and ground-based measurements of air temperature remains largely unresolved. In this study, we draw on an unusually robust and spatially exhaustive dataset of air temperature in two distinct bioclimates—Portland, Oregon, USA and Washington, D.C., USA—to evaluate the role of land cover on temperature. Our findings suggest that LST in highly built environments is consistently higher than recorded air temperatures, at times varying upwards of 15-degree Celsius, while forested areas contain between 2.5 and 3.5-degree Celsius lower temperatures than LST would otherwise indicate. Furthermore, our analyses points to the effects of land use and land cover features and other geophysical processes may have in determining differences in heat measurements across the two locations. The strength of the present analyses also highlights the importance of hyperlocal scales of data used in conjunction with coarser grain satellite derived data to inform urban heat assessments. Our results suggest a consistent pattern in both study areas, which can further our capacity to develop predictive models of air temperature using freely available descriptions of LST.