Supplementary material to "Strategies of Method Selection for Fine Scale PM2.5 Mapping in Intra-Urban Area Under Crowdsourcing Monitoring"

Abstract. Fine particulate matter (PM2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 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 PM2.5 mapping in an intra-urban area of China. During this process, PM2.5 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 PM2.5 concentrations in micro-environments varied in the intra-urban area. These local PM2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM2.5 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 R2: 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 period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PM2.5 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 PM2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

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Strategies of Method Selection for Fine Scale PM2.5 Mapping in Intra-Urban Area Under Crowdsourcing Monitoring

10.5194/amt-2018-402 ◽

2019 ◽

Author(s):

Shan Xu ◽

Bin Zou ◽

Yan Lin ◽

Xiuge Zhao ◽

Shenxin Li ◽

...

Keyword(s):

Urban Area ◽

Significant Risk ◽

Mapping Method ◽

Pollution Monitoring ◽

Selection Strategy ◽

Air Pollution Monitoring ◽

Method Selection ◽

Period 2 ◽

Sampling Campaign ◽

Selection For

Abstract. Fine particulate matters (PM2.5) are of great concern to public due to their significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 concentrations at unobserved locations due to the sparse fixed monitoring stations. On the other hand, as the rising of low-cost sensing for air pollution monitoring, crowdsourcing activities has been gradually introduced into fine exposure control in cities. However, the optimal mapping method for conventional sparse fixed measurements may not suit this new high-density monitoring way. This study therefore for the first time presents a crowdsourcing sampling campaign and strategies of method selection for hundred meter-scale level PM2.5 mapping in intra-urban area of China. In this process, the crowdsourcing sampling campaign was developed through a group of volunteers and their smart phone applications; the best performed mapping approach was chosen by comparing three widely used modelling method (ordinary kriging (OK), land use regression (LUR), and universal kriging combined OK and LUR (UK)) with increasing training sites. Results show that crowdsourcing based PM2.5 measurements varied significantly by sites (i.e. urban microenvironments) (Period 1: 28–136 µg m−3; Period 2: 115–266 µg m−3) and clearly differed from those at national monitoring sites (Period 1: 20–58 µg m−3; Period 2: 146–219 µg m−3). Despite the performance of the three models in estimating PM2.5 concentrations all improved as the number of training sites increase, OK interpolation performed best under conditions with non-peak traffic (9:00–11:00) in Period 1 (i.e. light-polluted period) with the hold-out validation R2 ranging from 0.47 to 0.82. Meanwhile, the accuracy of UK was the highest for 8:00 and 12:00 with less than 70 % training sites (0.40–0.69) and all five hours of Period 2 (i.e. heavy-polluted period) (0.32–0.68). Comparatively, LUR demonstrated limited ability in PM2.5 concentration simulations (0.04–0.55). Moreover, spatial distributions of PM2.5 concentrations based on the selected model with crowdsourcing data clearly illustrated their hourly intra urban variations which are generally concealed by the results from national air quality monitoring sites. This method selection strategy provides solid experimental evidence for method selection of PM2.5 mapping under crowdsourcing monitoring and a promising access to the prevention of exposure risks for individuals in their daily life.

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Strategies of Method Selection for Fine Scale PM2.5 Mapping in Intra-

10.5194/amt-2018-402-rc3 ◽

2019 ◽

Author(s):

Anonymous

Keyword(s):

Fine Scale ◽

Method Selection ◽

Selection For

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Supplementary material to "Statistical characteristics of raindrop size distribution during rainy seasons in Beijing urban area and implications for radar rainfall estimation"

10.5194/hess-2019-210-supplement ◽

2019 ◽

Author(s):

Yu Ma ◽

Guangheng Ni ◽

V. Chandrasekar ◽

Fuqiang Tian ◽

Haonan Chen

Keyword(s):

Size Distribution ◽

Urban Area ◽

Statistical Characteristics ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Raindrop Size Distribution ◽

Supplementary Material ◽

Raindrop Size

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Measuring Community Green Inequity: A Fine-Scale Assessment of Beijing Urban Area

Land ◽

10.3390/land10111197 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1197

Author(s):

Yuyang Zhang ◽

Qilin Wu ◽

Lei Wu ◽

Yan Li

Keyword(s):

Urban Area ◽

Evaluation System ◽

Large Scale ◽

Green Space ◽

Community Gardens ◽

Data Availability ◽

Green Spaces ◽

Fine Scale ◽

Macro Scale ◽

Community Residents

Green space exposure is beneficial to the physical and mental health of community residents, but the spatial distribution of green space is inequitable. Due to data availability, green equality or justice studies typically use administrative units as contextual areas to evaluate green spaces exposure, which is macro-scale and may lead to biased estimates as it ignores fine-scale green spaces (e.g. community gardens, lawns), that community residents are more frequently exposed to. In this study, we used the community as the unit of analysis, considered the green exposure of community residents in their daily social and physical activities, obtained data on three types of green spaces including fine-scale green spaces in the communities, surrounding large-scale parks and streetscape images. We propose a series of metrics for assessing community green equity, including a total of 11 metrics in three major categories of morphology, visibility and accessibility and applied them to 4,544 communities in Beijing urban area. Through spatial visualization, spatial clustering, radar plots, and correlation analysis, we comprehensively analyzed the equity of green space at the community scale, identified the cold and hot spots of homogeneity, and then analyzed the equity of green space among regions under the urbanization process. The measurement results of these metrics showed that there are large differences and complementarities between different categories of metrics, but similarities exist between metrics of the same category. The proposed methodology represents the development of a green space evaluation system that can be used by decision makers and urban green designers to create and maintain more equitable community green spaces. In addition, the large-scale, comprehensive and fine-scale green space measurement of this study can be combined with other studies such as public health and environmental pollution in the future to obtain more comprehensive conclusions and better guide the construction and regeneration of green spaces.

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Evaporation from a large lowland reservoir – (dis)agreement between evaporation models from hourly to decadal timescales

Hydrology and Earth System Sciences ◽

10.5194/hess-24-1055-2020 ◽

2020 ◽

Vol 24 (3) ◽

pp. 1055-1072 ◽

Cited By ~ 4

Author(s):

Femke A. Jansen ◽

Adriaan J. Teuling

Keyword(s):

Land Surface ◽

Positive Trend ◽

Hydrological Models ◽

Water Losses ◽

Surface Evaporation ◽

Diurnal Cycles ◽

Method Selection ◽

Lowland Reservoir ◽

Selection For ◽

Evaporation Models

Abstract. Accurate monitoring and prediction of surface evaporation become more crucial for adequate water management in a changing climate. Given the distinct differences between characteristics of a land surface and a water body, evaporation from water bodies requires a different parameterization in hydrological models. Here we compare six commonly used evaporation methods that are sensitive to different drivers of evaporation, brought about by a different choice of parameterization. We characterize the (dis)agreement between the methods at various temporal scales ranging from hourly to 10-yearly periods, and we evaluate how this reflects in differences in simulated water losses through evaporation of Lake IJssel in the Netherlands. At smaller timescales the methods correlate less (r=0.72) than at larger timescales (r=0.97). The disagreement at the hourly timescale results in distinct diurnal cycles of simulated evaporation for each method. Although the methods agree more at larger timescales (i.e. yearly and 10-yearly), there are still large differences in the projected evaporation trends, showing a positive trend to a more (i.e. Penman, De Bruin–Keijman, Makkink, and Hargreaves) or lesser extent (i.e. Granger–Hedstrom and FLake). The resulting discrepancy between the methods in simulated water losses of the Lake IJssel region due to evaporation ranges from −4 mm (Granger–Hedstrom) to −94 mm (Penman) between the methods. This difference emphasizes the importance and consequence of the evaporation method selection for water managers in their decision making.

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