Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring

Kerckhoffs, Jules; Hoek, Gerard; Vlaanderen, Jelle; Nunen, Erik van; Messier, Kyle P.; Brunekreef, Bert; Gulliver, John; Vermeulen, Roel

doi:10.1016/j.envres.2017.08.040

Cited by 56 publications

(50 citation statements)

References 48 publications

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“…Recent studies have recognized this issue and have suggested methods to adjust temporal variation and to increase the ability of mobile measurements to quantify spatial variation in longer-term average concentrations. 11–16…”

Section: Introductionmentioning

confidence: 99%

Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles

Tessum

Larson

Gould

et al. 2018

Environ. Sci. Technol.

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Mobile monitoring and fixed-site monitoring using passive sampling devices (PSD) are popular air pollutant measurement techniques with complementary strengths and weaknesses. This study investigates the utility of combining data from concurrent 2-week mobile monitoring and fixed-site PSD campaigns in Los Angeles in the summer and early spring to identify sources of traffic-related air pollutants (TRAP) and their spatial distributions. There were strong to moderate correlations between mobile and fixed-site PSD measurements of both NO and NO in the summer and spring (Pearson's r between 0.43 and 0.79), suggesting that the two data sets can be reliably combined for source apportionment. PCA identified the major TRAP sources as light-duty vehicle emissions, diesel exhaust, crankcase vent emissions, and an independent source of combustion-derived ultrafine particle emissions. The component scores of those four sources at each site were significantly correlated across the two seasons (Pearson's r between 0.58 and 0.79). Spatial maps of absolute principal component scores showed all sources to be most prominent near major roadways and the central business district and the ultrafine particle source being, in addition, more prominent near the airport. Mobile monitoring combined with fixed-site PSD sampling can provide high spatial resolution estimates of TRAP and can reveal underlying sources of exposure variability.

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Section: Introductionmentioning

confidence: 99%

Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles

Tessum

Larson

Gould

et al. 2018

Environ. Sci. Technol.

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show abstract

“…We present a model that shows a significant relationship between the forest patches and the land use factors. Whereas relative measures have been used to show the robustness of similar models in previous studies [40][41][42], we used the area under the curve or the ROC and other measures to determine the robustness of the model. The ROC statistic of 0.995 suggests a near-perfect model fit [43], and thus, the resultant model achieved in the modelling process is not a result of a random chance.…”

Section: Discussionmentioning

confidence: 99%

Protecting the patches from the footprints: examining the land use factors associated with forest patches in Atewa range forest reserve

2021

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Background Land use practices are noted to contribute to changes in forest landscape composition. However, whereas studies have reported the intermix of land uses and forest patches and measured the direct impacts of land uses on forest patches, little is known regarding the spatially-explicit association between the most recent forest patches and land use footprints in protected areas. In this study, we use methods from GIS, remote sensing, and statistics to model the spatial relationship between footprints of land uses and patches of forest cover by drawing on geospatial data from the Atewa range forest reserve (ARFR). Results The study finds that forest patches that are within 1 km from agricultural land use footprints (AOR = 86.625, C.I. 18.057–415.563, P = 0.000), logging sites (AOR = 55.909, C.I. 12.032–259.804, P = 0.000), mine sites (53.571, C.I. 11.287–254.255, P = 0.000), access roads (AOR = 24.169, C.I. 5.544–105.357, P = 0.000), and human settlement footprints (AOR = 7.172, C.I. 1.969–26.128, P = 0.003) are significantly more likely to be less than the mean patch area (375,431.87 m2 = 37.54 ha) of forest cover. A ROC statistic of 0.995 achieved in this study suggests a high predictive power of the proposed model. Conclusion The study findings suggest that to ensure sustainable land uses and ecological integrity, there is a need for land use policies and land management strategies that ensure responsible livelihood activities as well as further restrictions on logging and mining in the globally significant biodiversity area.

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“…The concerns that measurements are made on roadways and that there is relatively little data at each point can be alleviated by employing a mobile design whereby the monitoring vehicle is parked at specified residential locations for fixed periods of time rather than relying on measurements made only while on the move. Such a modification to the mobile monitoring design has been used successfully [ 43 , 44 , 45 ]. There are precedents for using a mobile monitoring platform in China, and in Beijing in particular [ 46 , 47 ].…”

Section: Approaches For Estimating Exposurementioning

confidence: 99%

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

Vedal

Han

Jia

et al. 2017

IJERPH

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No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

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Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring

Cited by 56 publications

References 48 publications

Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles

Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles

Protecting the patches from the footprints: examining the land use factors associated with forest patches in Atewa range forest reserve

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

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