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China implemented systematic air pollution control measures during the 2008 Beijing Summer Olympics and Paralympics to improve air quality. This study used an innovative mobile laboratory to conduct in situ monitoring of on-road air pollutants along Beijing's 4th Ring Road on 31 selected days before, during, and after the Olympics air pollutio...
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Citations
... Meanwhile, RAQMS stations are sparsely dispersed, so the system cannot capture air pollution or traffic volumes along many roads [4,8]. To compensate for these shortcomings, a mobile laboratory (ML) was introduced in 2011 in Korea to investigate the characteristics of roadside air pollution sources and the spatiotemporal distribution of air pollutants in urban areas [4,[9][10][11][12]. MLs are deployed in vehicles, and can survey roads not covered by the monitoring networks, analyze pollution levels in areas around industrial complexes, and conduct real-time surveys of redispersed road dust. ...
Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emissions sources. In South Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.
... Therefore, the performance of Grimm is not affected by vehicle speed in our mobile monitoring. Using the same principle, Wang et al. [66] used Grimm in a mobile laboratory to monitor on-road air pollutants during the Beijing 2008 summer Olympics. Elen et al. [67] used Grimm on bicycles to monitor PM. ...
Low-cost sensors (LCS) are affordable, compact, and often portable devices designed to measure various environmental parameters, including air quality. These sensors are intended to provide accessible and cost-effective solutions for monitoring pollution levels in different settings, such as indoor, outdoor and moving vehicles. However, the data produced by LCS is prone to various sources of error that can affect accuracy. Calibration is a well-known procedure to improve the reliability of the data produced by LCS, and several developments and efforts have been made to calibrate the LCS. This work proposes a novel Estimated Error Augmented Two-phase Calibration (
EEATC
) approach to calibrate the LCS in stationary and mobile deployments. In contrast to the existing approaches, the
EEATC
calibrates the LCS in two phases, where the error estimated in the first phase calibration is augmented with the input to the second phase, which helps the second phase to learn the distributional features better to produce more accurate results. We show that the
EEATC
outperforms well-known single-phase calibration models such as linear regression models (single variable linear regression (SLR) and multiple variable linear regression (MLR)) and Random forest (RF) in stationary and mobile deployments. To test the
EEATC
in stationary deployments, we have used the Community Air Sensor Network (CAIRSENSE) data set approved by the United States Environmental Protection Agency (USEPA), and the mobile deployments are tested with the real-time data obtained from SensurAir, an LCS device developed and deployed on moving vehicle in Chennai, India.
... Therefore, the performance of Grimm is not affected by vehicle speed in our mobile monitoring. Using the same principle, Wang et al. [16] used Grimm in a mobile laboratory to monitor onroad air pollutants during the Beijing 2008 summer olympics. Elen et al. [17] used Grimm on bicycles to monitor PM. ...
p>In vehicular mobility applications, sensor devices are exposed to rapidly varying concentrations of pollutants. Therefore, more than a conventional data set containing features such as sensor output, temperature and humidity is required to calibrate mobile sensors. We propose a new data set having additional features to address possible error sources encountered in vehicular-mobility LCS applications. We show that the proposed data set is a better choice for calibrating mobile LCS devices when compared to the conventional data set. Further, we propose and investigate two different tandem configurations involving a two-phase calibration approach to improve the calibration accuracy of mobile sensors. The calibration is done with real-time data obtained from an LCS device, SensurAir, which we developed and deployed in Chennai, India. </p
... Therefore, the performance of Grimm is not affected by vehicle speed in our mobile monitoring. Using the same principle, Wang et al. [16] used Grimm in a mobile laboratory to monitor onroad air pollutants during the Beijing 2008 summer olympics. Elen et al. [17] used Grimm on bicycles to monitor PM. ...
p>In vehicular mobility applications, sensor devices are exposed to rapidly varying concentrations of pollutants. Therefore, more than a conventional data set containing features such as sensor output, temperature and humidity is required to calibrate mobile sensors. We propose a new data set having additional features to address possible error sources encountered in vehicular-mobility LCS applications. We show that the proposed data set is a better choice for calibrating mobile LCS devices when compared to the conventional data set. Further, we propose and investigate two different tandem configurations involving a two-phase calibration approach to improve the calibration accuracy of mobile sensors. The calibration is done with real-time data obtained from an LCS device, SensurAir, which we developed and deployed in Chennai, India. </p
... High intensity pollutant discharge is the determinative cause of poor air quality (Zhang et al. 2009a, b;Wang and Hao 2012). To reduce pollutant concentrations and improve the air quality, the Chinese government has issued a series of clean air policies since 2008 (Wang et al. 2009(Wang et al. , 2011China's State Council 2013;China's State Council 2018). Through these persistent efforts toward reducing pollutant emissions, the air quality has shown significant improvement in Beijing and its surroundings in recent years (Wang et al. 2019b, c;Zhang et al. 2019a, b). ...
Primary work on the planetary boundary layer (PBL) climatic features and associated air pollution in Beijing was carried out based on a 40-year dataset. The main results are identified as follows. The annual mean PBL height was 906 m (1455 m in daytime and 357 m in nighttime). Within the PBL, the average potential temperature, specific humidity, and wind speed were 287.3 K, 5.1 g kg−1, and 6.5 m s−1. Significant seasonal variations were observed in the PBL variables, leading to distinct pollutant diffusion conditions. The PM2.5 concentrations varied seasonally, with a high value in winter (97 µg m−3) and a low value in summer (74 µg m−3). The summertime PM2.5 was closely related to the local emission intensity (R = 0.79) and declined with a reduction in the emission intensity, while the winter PM2.5 showed large interannual variabilities and trends, relying more on the PBL conditions. A comparison of polluted and clean winters was performed, revealing that polluted winters were associated with a warmer layer above the PBL that inhibited the development of the PBL (933 m vs. 1283 m, daily maximum height for polluted vs. clean winters) and led to a humid (1.4 g kg−1 vs. 0.8 g kg−1) and weak wind (5.8 m s−1 vs. 8.2 m s−1) layer within the PBL. In terms of synoptic impacts, the warm advection above the PBL (0.07 K h−1) and subsidence heating (0.21 K h−1) near the top of the PBL contributed to the occurrence of a stable and polluted PBL.
... Once set up, air quality measurements can be made while the vehicle is in motion, thus allowing for enhanced spatial and temporal measurement resolution (i.e., the capacity to take new measurements in short time intervals). This approach has been used to measure on-road air pollution (Wang et al., 2009;Canagaratna et al., 2004) as well as ambient pollution concentrations in a range of environments (Maciejczyk et al., 2004;Herndon et al., 2005), among other applications. ...
The deployment of a mobile air quality monitoring laboratory requires advanced real-time instrument monitoring and data management software, which can be prohibitively expensive. In this work we present the PLUME Dashboard: a software package built in Python designed specifically for mobile air quality monitoring purposes. It aims to provide a free and open-source alternative to comparable commercial packages, thus reducing the barrier to entry of conducting such research. This paper outlines the development of the PLUME Dashboard and justifies the design choices that were made while also providing thorough documentation and explanation for how the software works. Functionality includes real-time data display, real-time peak identification, baseline subtraction, real-time air quality and self-sampling alerts (based on wind direction and vehicle speed), and post-processing tools such as peak identification and map merging with GPS data. The functionality of PLUME Dashboard is tested using real-world data collected in Toronto and Vancouver Canada.
... The application of methods for measuring toxic concentrations in the environment (Isakov et al., 2007) is a great help within the project, in addition to benchmarks in which the applications of similar work (Wang et al., 2009) make the proposed design of the project feasible and also considering the mathematical models Muntean, 1999) and application of commercial products (Amanatidis et al., 2013). In this work, four phases are considered in the design and implementation processes. ...
Valuing public perceptions of biophilia impact on human well-being: 2 sustainable building case studies from India and Greece|
This study focusses on valuing the ‘green technologies’ of designing and building with nature to encourage a wider dimension to the current ratings and evaluations of effectiveness of ‘green buildings’, by including the perceived impact on human well-being.
We believe that for buildings to offer a ‘sustainable’ way of living, they must also include the technologies and intelligence to provide what all of life needs to thrive beyond just surviving.
This paper aims to give a wider understanding of ‘green buildings’ beyond reporting on energy, water and waste, to show a more sophisticated, wider evaluation of sustainable buildings by including the value of subjective perception of individuals’ experience. And, to contribute to changing existing paradigms about how ‘green buildings’ are valued.
Other studies conclude that leading bodies for ‘green building’ certification have failed to provide a holistic measure of sustainable buildings. Current environmental measures of ‘green buildings’ conflict with the values of human health and there are conflicting ‘logics’ and technologies with little consensus on what makes a sustainable building.
The perceived ‘value’ of the health and well-being benefits of a ‘green building’ appears to be disregarded as a measure of effectiveness. This paper challenges that view.
Findings from questionnaires, testimonials and in-depth interviews from the public using 2 green buildings in different countries suggest that people do believe that they experience physical and emotional health benefits from spending time in in green buildings.
This suggests that valuing the ’unmeasurable’ perceived benefits of sustainable buildings on health and well-being, equally alongside quantitative audits and environmental measures, could bring combined societal and environmental benefits.
More study and evaluation with larger samples in different countries is necessary.
Further study could make an important contribution to greater understanding about the positive impacts of biophilia design for healthcare institutions, community spaces, workplaces and homes.
... The use of such portable facilities has been noted by several journals. For example, Wang et al. (2009) reported on the collection of the air quality information from road sites for the Beijing Olympic Games (held during 2008). Padró-Martínez et al. (2012) used a portable surveillance platform to quantify air pollution concentrations in a near-road urban atmosphere with a broad range of traffic and weather conditions which was mounted with tools for rapid response. ...
In major cities, air quality is of significant concern because of its negative effect on the health of the region’s living conditions, climate, and economy. Recent studies show the significance of the data on microlevel pollution which includes severe air pollutants and their impacts on human. Conventional methods of measuring air quality need skilled personnel for accurate data measurement that are based on stationary and limited measuring station networks. However, it is costly to seize the spatio-temporal variability and to recognize pollution hotspots that are necessary to develop real-time exposure control strategies. Due to the restricted accessibility of information and the non-scalability of standard techniques for air pollution monitoring, a real-time system with both higher spatial and temporal resolution is crucial. In recent times, unmanned aerial vehicles (UAVs) mounted with various sensors have been implemented for on-site air quality surveillance as they can offer new methods and research possibilities in air pollution and emission tracking, as well as in the study of environmental developments. An extensive literature review has been conducted, and it was observed that there are types of UAVs and types of sensors that are used for air quality monitoring for the parameters like CO, SO2, NO2, O3, PM2.5, PM1.0, and black carbon. Low-cost wireless sensors have been using for monitoring purpose in the past studies, and when results obtained are validated with the stationary monitoring instruments, the coefficient of correlation (R²) is found to be varied from 0.3 to 0.9. The difficulties, however, are not just technical, but at present time, policies and laws, which vary from country to country, symbolize the major challenge to the extensive use of UAVs in air quality/monitoring studies.
... The application of methods for measuring toxic concentrations in the environment (Isakov et al., 2007) is a great help within the project, in addition to benchmarks in which the applications of similar work (Wang et al., 2009) make the proposed design of the project feasible and also considering the mathematical models Muntean, 1999) and application of commercial products (Amanatidis et al., 2013). In this work, four phases are considered in the design and implementation processes. ...
Recent studies have defined the ‘healthy neighborhood’ as the social or socio-economic unit within a healthy district or spatial unit within a human-oriented transportation system. However, those views have failed to notice both standard elements of organic and city transportation policy and other dimensions of organic transportation, excluding spatial one. In this study, the social-ecological system (SES) and human-oriented transportation system (HOTS) frameworks will be compared to each other in terms of structure, application and dynamics to draw a conclusion about the suitability of HOTS in framework to describe the complex socio-technological system such as ‘healthy neighborhood’. Additionally, the structure and the multidisciplinary process that occur within the ‘healthy neighborhood’ will be analyzed in terms of HOTS framework. Finally, the indicators of pattern and size of ‘healthy neighborhood’ in terms of HOTS framework will be suggested. Thus, a healthy interdisciplinary neighborhood will be captured. This research will be the first attempt to shift from the traditional ‘unit’ perspective to the network models capable of unfolding the internal socio-economic and technical processes, uncovering the internal organization and functions of healthy neighborhoods.
... The application of methods for measuring toxic concentrations in the environment (Isakov et al., 2007) is a great help within the project, in addition to benchmarks in which the applications of similar work (Wang et al., 2009) make the proposed design of the project feasible and also considering the mathematical models Muntean, 1999) and application of commercial products (Amanatidis et al., 2013). In this work, four phases are considered in the design and implementation processes. ...