Strategies of method selection for fine-scale PM<sub>2.5</sub> mapping in an intra-urban area using crowdsourced monitoring

Abstract

Fine particulate matter (PM 2.5 ) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM 2.5 concentrations in unobserved locations due to the sparse number of fixed monitoring stations. Due to an increase in low-cost sensing for air pollution monitoring, crowdsourced monitoring of exposure control has been gradually introduced into cities. However, the optimal mapping method for conventional sparse fixed measurements may not be suitable for this new high-density monitoring approach. This study presents a crowdsourced sampling campaign and strategies of method selection for 100 m scale PM 2.5 mapping in an intra-urban area of China. During this process, PM 2.5 concentrations were measured by laser air quality monitors through a group of volunteers during two 5 h periods. Three extensively employed modelling methods (ordinary kriging, OK; land use regression, LUR; and regression kriging, RK) were adopted to evaluate the performance. An interesting finding is that PM 2.5 concentrations in microenvironments varied in the intra-urban area. These local PM 2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM 2.5 concentration mapping should be adjusted according to the changing sampling and pollution circumstances. During this project, OK interpolation performs best in conditions with non-peak traffic situations during a lightly polluted period (holdout validation R 2 : 0.47-0.82), while the RK modelling can perform better during the heavily polluted period (0.32-0.68) and in conditions with peak traffic and relatively few sampling sites (fewer than ∼ 100) during the lightly polluted pe-riod (0.40-0.69). Additionally, the LUR model demonstrates limited ability in estimating PM 2.5 concentrations on very fine spatial and temporal scales in this study (0.04-0.55), which challenges the traditional point about the good performance of the LUR model for air pollution mapping. This method selection strategy provides empirical evidence for the best method selection for PM 2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

A methodology for the design of an effective air quality monitoring network in port areas

“…However, the shipping traffic emissions could be variable in other periods and the conclusions might be different. Considering the high cost of certified reference instruments, there is a current trend worldwide to increase the spatial and temporal data resolution and range using low-cost air pollutant sensors/monitors 27,28 . Utilizing low-cost air quality platforms in data collecting would be helpful in more adoptive air quality monitoring network design.…”

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

“…Further study will focus on improving the model ability to learn rare features, and we will look to explore the intensive observations data for monitoring the air pollutants [57,58]. It is worthwhile to integrate remote sensing and social sensing for spatiotemporal estimation of air pollution based on advanced statistical methods.…”

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

“…Focusing on pedestrians' well-being, human perception involves different spheres, and thus scientific effort includes data collection related to (i) urban air quality, (ii) noise pollution, (iii) and outdoor thermal comfort. An overview of the most recent studies [114,115,116,117,118,119,120,121,122,123,124,125,126,127] involving these types of data collection at the urban scale is presented distinguishing among different involved monitoring systems as summarized in Table 2.…”

“…Sensor networks -Real time noise/pollution measures for population alert [115] -Fine-grained city air quality map through automobile built-in sensors [119] -Visualize air pollution propagation [118] -IoT platform for public consultancy of air quality [116] -Prototype of IoT-based technology for noise and air quality pollution real-time monitoring [117] Sensor & Social media -Monitoring and mitigation of urban noise pollution [126] Environmental sensor & Survey -Investigation of dynamic thermal comfort [128] -Extreme learning machine approach to predict thermal comfort in outdoors [129] Wearables -Map PM2.5 distribution through miniaturized, personal devices [121] -Map transient outdoor comfort [123] -Understanding dynamic thermal comfort [124] -Environmental mapping according to pedestrian perspective [130] -Enhancement of crowd-sourcing air quality through low costs participating [120] Wearables & Social media -Evaluation and representation of sound environment [130] -Soundscapes related to people perception [127] -Sound classification and mapping [131] Wearables & physiological data -Physiological response to different microclimates [132]…”