Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment

Su, Jason; Jerrett, Michael; Meng, Ying‐Ying; Pickett, Melissa; Ritz, Beate

doi:10.1016/j.scitotenv.2014.11.022

Cited by 55 publications

(38 citation statements)

References 36 publications

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“…The temporal variability of TRAP concentrations have also been incorporated into LUR models through the addition of mobile or continuous monitoring allowing for short-term and daily estimates of TRAP exposure for study participants (44, 55–58). New data inputs for LUR models, including satellite-derived pollutant measurements (59, 60) and the development of hybrid models combining LUR with Bayesian Maximum Entropy and other statistical approaches have also improved the accuracy of TRAP exposure assessment (61, 62) In studies with available participant-reported time spent in locations outside the home, LUR models have been used to derive time-weighted estimates of exposure based on location (40) More recent application of this time-weighted approach have utilized smartphones and GPS-derived location data to improve estimates of TRAP exposure by combining LUR or other modeled TRAP estimates with individuals’ location through space and time (63)…”

Section: Assessment Of Trap Exposurementioning

confidence: 99%

Air pollution and allergic diseases

Brandt¹,

Myers²,

Ryan

et al. 2015

Current Opinion in Pediatrics

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Purpose of review Exposure to traffic-related air pollutants (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant because increasingly large segments of the population worldwide reside in zones that have high levels of TRAP (1), including children since schools are often located in high traffic pollution exposure areas. Recent findings Recent findings include epidemiologic and mechanistic studies that shed new light on the impact of traffic pollution on allergic diseases and the biology underlying this impact. In addition, new innovative methods to assess and quantify traffic pollution have been developed to assess exposure and identify vulnerable populations and individuals. Summary This review will summarize the most recent findings in each of these areas. These findings will have substantial impact on clinical practice and research by development of novel methods to quantify exposure and identify at-risk individuals, as well as mechanistic studies that identify new targets for intervention for individuals most adversely affected by TRAP exposure.

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Section: Assessment Of Trap Exposurementioning

confidence: 99%

Air pollution and allergic diseases

Brandt¹,

Myers²,

Ryan

et al. 2015

Current Opinion in Pediatrics

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“…Mobile phone technology may help to overcome the previous limitations because of its widespread use around the world and the combination of assisted GPS technology and network positioning systems [13-16]. The assisted GPS technology makes use of remote GPS location servers to reduce both power consumption and the time to first fix position [14].…”

Section: Introductionmentioning

confidence: 99%

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Donaire-González¹,

Valentín²,

Nazelle³

et al. 2016

JMIR Mhealth Uhealth

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BackgroundTracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures.ObjectiveThe objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern.MethodsAdults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1).ResultsThe mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812).ConclusionsThe use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.

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“…A distance decay curve was first generated to visualize the correlations between air pollution and land cover categories, which were generated through the object-based classification approach. The LUR model was then run through a deletion/substitution/addition (DSA) machine learning algorithm (Beckerman et al, 2013;Su et al, 2015c) using the aggregated nine land cover categories described above. The DSA algorithm is an aggressive model search algorithm which iteratively generates polynomial generalized linear models based on the existing terms in the current 'best' model and the following three steps:…”

Section: Air Pollution Data and Lur Modelingmentioning

confidence: 99%

Application of land use regression techniques for urban greening: An analysis of Tianjin, China

Guo

Dong

et al. 2019

Urban Forestry & Urban Greening

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The land cover types in a 4 km by 4 km space centered around an air quality monitoring station in the city of Tianjin were classified using the e-Cognition software. We identified 23 air quality monitoring sites and their surrounding land cover types. The suitability of using land cover types to predict traffic-related air pollution Nitrogen Dioxide (NO 2) was tested through a machine learning land use regression (LUR) modeling technique. We found that vegetation was significantly but negatively associated with air pollution levels (correlation coefficient r = −0.5, p < 0.01) while highways and major roadways were positively and significantly associated with air pollution levels (r = 0.60, p < 0.01). The LUR model explained 84% of the total variance in measured NO 2 concentrations. The associations of the land cover types with NO 2 concentrations were then used in land cover retrofitting strategies to quantify the potential for reducing traffic-related air pollution. We identified that the improvements in air quality could reach 25% if specific urban greening strategies were fully implemented. The results of the study can help policy makers and environmental designers adopt effective air pollution reduction policies to improve air quality and protect public health in Tianjin and other urban regions in China.

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Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment

Cited by 55 publications

References 36 publications

Air pollution and allergic diseases

Air pollution and allergic diseases

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Application of land use regression techniques for urban greening: An analysis of Tianjin, China

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