Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors

Minet, Laura; Gehr, Ronald; Hatzopoulou, Marianne

doi:10.1016/j.envpol.2017.06.071

Cited by 45 publications

(28 citation statements)

References 37 publications

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“…O 3 concentrations were monitored using 24 portable O 3 monitors (Aeroqual Series 500, Auckland, New Zealand) having a detection range of 0-1000 µg m −3 , a resolution of 2 µg m −3 , and an accuracy of within ± 16 µg m −3 from 0 to 200 µg m −3 and ± 20 µg m −3 from 200 to 1000 µg m −3 [29,30]. The monitor had proven to be reliable and accurate in numerous previous studies [31][32][33]. Micrometeorological parameters (AT, RH, WS, and SR) were monitored using a pocket weather station (Kestrel 5500, Nielsen-Kellerman, Boothwyn, PA, USA) and a digital luminance meter (TES-1332A, TES Electrical Electronic Corp., Taipei, Taiwan).…”

Section: Monitoring Methods and Datesmentioning

confidence: 99%

Spatiotemporal Ozone Level Variation in Urban Forests in Shenzhen, China

Duan

Wang

Pei

et al. 2019

Forests

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This study is among the first to investigate ozone levels in urban forests in China. It establishes that urban forest air quality in Yuanshan Forest Park (Shenzhen) is suitable for recreational activities and identifies spatial, seasonal, and diurnal O3 patterns and relationships with micrometeorological parameters, suggesting the possibility of manipulating relevant forest characteristics to reduce Surface ozone (O3) levels. An understanding of O3 levels of urban forest environments is needed to assess potential effects on human health and recreational activities. Such studies in China are scarce. This study investigated urban forest O3 levels to improve understanding and support residents engaging in forest recreational activities. We monitored O3 levels in 2015–2016 for three urban forests representing common habitats (foothill, valley, and ridge) in Yuanshan Forest Park and for an adjacent square. The overall mean daily and daily maximum 8 h mean (MDA8) O3 concentrations were highest for the ridge forest and lowest for the valley forest. Each forest’s O3 concentrations were highest in summer. Diurnally, forest O3 concentrations peaked between 13:00 and 17:00 and reached a minimum between 03:00 and 09:00. The correlation between forest O3 concentrations and air temperature (AT) was strongly positive in summer and autumn but negative in spring. In each season, O3 concentration was negatively correlated with relative humidity (RH). No MDA8 or hourly O3 concentrations in the forests exceeded National Ambient Air Quality Standard Grade I thresholds (100 and 160 μg m−3, respectively). O3 accumulation is present in ridge urban forest in all seasons. Foothill and valley urban forests have better air quality than ridge forestation. Urban forest air quality is better in spring and autumn than in summer and is better from night-time to early morning than from noon to afternoon.

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Section: Monitoring Methods and Datesmentioning

confidence: 99%

Spatiotemporal Ozone Level Variation in Urban Forests in Shenzhen, China

Duan

Wang

Pei

et al. 2019

Forests

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“…No O 3 sensor was available during this data collection, but we propose another approach based on statistical methods to control its contribution to the measurements (see Sections 3.3 and 5.1). Aeroqual Series 500 monitors have largely been used in numerous studies on individual exposure or air pollution mapping, e.g., [11,19,[61][62][63].…”

Section: Primary Data Collection and Structurationmentioning

confidence: 99%

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Gelb

Apparicio

2020

Atmosphere

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Cyclists are particularly exposed to air and noise pollution because of their higher ventilation rate and their proximity to traffic. However, few studies have investigated their multi-exposure and have taken into account its real complexity in building statistical models (nonlinearity, pseudo replication, autocorrelation, etc.). We propose here to model cyclists’ exposure to air and noise pollution simultaneously in Paris (France). Specifically, the purpose of this study is to develop a methodology based on an extensive mobile data collection using low-cost sensors to determine which factors of the urban micro-scale environment contribute to cyclists’ multi-exposure and to what extent. To this end, we developed a conceptual framework to define cyclists’ multi-exposure and applied it to a multivariate generalized additive model with mixed effects and temporal autocorrelation. The results show that it is possible to reduce cyclists’ multi-exposure by adapting the planning and development practices of cycling infrastructure, and that this reduction can be substantial for noise exposure.

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“…For instance, Shi et al developed LUR models from their street-level estimation [33]. Minet et al presented LUR models for NO 2 exposure assessment, based on sub-segments, categorized in terms of the number of visits per road segment [119]. They also studied the influence of number of road segments and the number of visits per road segment on the performance of LUR models.…”

Section: Approaches For Mobile Monitoringmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

144

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Abstract:The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

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Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors

Cited by 45 publications

References 37 publications

Spatiotemporal Ozone Level Variation in Urban Forests in Shenzhen, China

Spatiotemporal Ozone Level Variation in Urban Forests in Shenzhen, China

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

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