Estimating daily surface NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; concentrations from satellite data –  a case study over Hong Kong using land use regression models

Anand, Jasdeep; Monks, Paul S.

doi:10.5194/acp-17-8211-2017

Cited by 39 publications

(21 citation statements)

References 48 publications

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“…As with studies in Europe [64][65][66][67][68], studies in China have reported that incorporating satellite-based estimates improved model performance of regional LUR models for NO 2 , PM 10 , and PM 2.5 [41,56]. More temporally-resolved models have also been reported; for example, using satellite AOD and VCDs, linear mixed-effects LUR models have been used to estimate daily average concentration of PM 10 [53] and NO 2 [51].…”

Section: Data Availability/accessibility Challengesmentioning

confidence: 94%

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Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Heal

Reis

2018

Atmosphere

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Rapid urbanization in China is leading to substantial adverse air quality issues, particularly for NO 2 and particulate matter (PM). Land-use regression (LUR) models are now being applied to simulate pollutant concentrations with high spatial resolution in Chinese urban areas. However, Chinese urban areas differ from those in Europe and North America, for example in respect of population density, urban morphology and pollutant emissions densities, so it is timely to assess current LUR studies in China to highlight current challenges and identify future needs. Details of twenty-four recent LUR models for NO 2 and PM 2.5 /PM 10 (particles with aerodynamic diameters <2.5 µm and <10 µm) are tabulated and reviewed as the basis for discussion in this paper. We highlight that LUR modelling in China is currently constrained by a scarcity of input data, especially air pollution monitoring data. There is an urgent need for accessible archives of quality-assured measurement data and for higher spatial resolution proxy data for urban emissions, particularly in respect of traffic-related variables. The rapidly evolving nature of the Chinese urban landscape makes maintaining up-to-date land-use and urban morphology datasets a challenge. We also highlight the importance for Chinese LUR models to be subject to appropriate validation statistics. Integration of LUR with portable monitor data, remote sensing, and dispersion modelling has the potential to enhance derivation of urban pollution maps.

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Section: Data Availability/accessibility Challengesmentioning

confidence: 94%

“…The latter requires manpower and material resources. Two studies in China achieved this by using a mixed linear regression (MLR) approach including satellite data [51,53]. LUR models are typically fixed-effect models, in which the predictor variables are temporally invariant.…”

Section: Temporal Variabilitymentioning

confidence: 99%

Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Heal

Reis

2018

Atmosphere

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“…This modelling approach was used in different regions, including Europe [52,53], United States (US) [54][55][56][57], China [58], Mexico city [59], and Israel [60,61] to estimate daily PM 10 and PM 2.5 concentrations in spatial resolution of 1 × 1 km 2 and showed good performance with cross validated (cv) total R 2 ranging between 0.70-0.92, depending on the area and the specific methodology that was applied. Similar approach was used to estimate daily NO 2 concentrations in the US [62], Hong Kong [63], and Switzerland [64] with cv R 2 of 0.79, 0.84, and 0.58, respectively.…”

Section: Remote Sensingmentioning

confidence: 99%

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

et al. 2020

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An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.

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“…Different air quality prediction methods, such as air dispersion models, atmospheric chemical transport models, satellite remote sensing and various statistical methods, have been developed and applied to estimate air pollution (Yim et al, 2019a, b;Tong et al, 2018a, b;Luo et al, 2018;Shi et al, 2020a) and population exposure (Gu and Yim 2016;Gu et al, 2018;Hao et al, 2016;Li et al, 2020;Hou et al, 2019;Michanowicz et al, 2016;Wang et al, , 2020Yim et al, 2019c). Among these exposure assessment methods, land-use regression (LUR) is a widely used modeling approach to characterize long-term-average air pollutant concentrations at a fine spatial scale, which provides high-spatial-resolution estimates of exposure for use in epidemiological studies (Bertazzon et al, 2015;Jones et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan

Chuang

et al. 2021

Atmos. Chem. Phys.

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Abstract. To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we developed and evaluated the intercity transferability of annual-average LUR models for ambient respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM10, PM2.5, NO2 and O3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM10, PM2.5, NO2 and O3 achieved relatively high prediction performance, with R2 values of > 0.72 and leave-one-out cross-validation (LOOCV) R2 values of > 0.53. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO2 and < 0.56 for the other three pollutants. The LUR-model-based 500 m × 500 m spatial-distribution maps of these air pollutants illustrated pollution hot spots and the heterogeneity of population exposure, which provide valuable information for policymakers in designing effective air pollution control strategies. The LUR-model-based air pollution exposure estimates captured the spatial variability in exposure for participants in a cohort study. This study highlights that LUR models can be reasonably established upon a routine monitoring network, but there exist uncertainties when transferring LUR models between nearby cities. To the best of our knowledge, this study is the first to evaluate the intercity transferability of LUR models in Asia.

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Estimating daily surface NO<sub>2</sub> concentrations from satellite data – a case study over Hong Kong using land use regression models

Cited by 39 publications

References 48 publications

Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

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Estimating daily surface NO&lt;sub&gt;2&lt;/sub&gt; concentrations from satellite data – a case study over Hong Kong using land use regression models

Cited by 39 publications

References 48 publications

Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

Development and intercity transferability of land-use regression models for predicting ambient PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations in northern Taiwan

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Estimating daily surface NO<sub>2</sub> concentrations from satellite data – a case study over Hong Kong using land use regression models

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan