Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology

Stafoggia, Massimo; Schwartz, Joel; Badaloní, Chiara; Bellander, Tom; Alessandrini, Ester; Cattani, Giorgio; Donato, Francesca De'; Gaeta, Alessandra; Leone, Gianluca; Lyapustin, A.; Sorek–Hamer, Meytar; Hoogh, Kees de; Di, Qian; Forastiére, Francesco; Kloog, Itai

doi:10.1016/j.envint.2016.11.024

Cited by 114 publications

(89 citation statements)

References 40 publications

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“…This tendency was reduced by the choice of the least squares loss function as describes in chapter 2.5.3 but likely still continues to affect the model accuracy. The model performance is comparable to similar studies, also in its underestimation of PM (Hu et al, ; Grange et al, ; Stafoggia et al, ; Zhang et al, ). Tenfold random train/test splits were conducted, resulting in 10 models.…”

Section: Resultssupporting

confidence: 81%

“…The data set has been successfully used in previous air quality studies (cf. Chen et al, ; Stafoggia et al, ; Zheng et al, ). To capture regional transport of particles, ERA‐Interim reanalysis wind components (m/s) in east‐west and north‐south direction (10 m height) are used.…”

Section: Methodsmentioning

confidence: 99%

“…While numerous air pollution studies apply statistical models mainly to accurately predict PM concentrations (Hu et al, ; Kloog et al, ; Stafoggia et al, ; van Donkelaar et al, ; Zhang et al, ), recent studies additionally analyzed feature importances and the information content of explanatory variables in the statistical models to infer processes. For example, Park et al () used a RF model to predict PM10 in South Korea and found large influence of AOD, day of the year (DOY), wind speed, and solar radiation on the modeled PM concentrations.…”

Section: Motivation and Research Questionsmentioning

confidence: 99%

“…Sixteen-day NDVI averages obtained from the MODIS MOD13Q1 Version 6 product are used (DOI: 10.5067/MODIS/MOD13Q1.006) to approximate photosynthetically active vegetation (Tucker C. J., 1979). The NDVI was found to influence PM concentrations in several previous works (Beloconi et al, 2018;Chudnovsky et al, 2014;Stafoggia et al, 2017). Vegetation acts as a sink by increasing the aerodynamic roughness and available surface for mechanical deposition (Fuzzi et al, 2015).…”

Section: Modis Ndvimentioning

confidence: 99%

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Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 μm (PM10) by combining satellite‐borne aerosol optical depth (AOD) with meteorological and land‐use parameters. The model is shown to accurately predict PM10 (overall R 2 = 0.77, RMSE = 7.44 μg/m 3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM10 in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM10 include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM10 concentrations are identified in winter and summer. In winter, mean predictions of PM10 concentrations >35 μg/m 3 occur when BLH is below ∼500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m 3 of PM10. In summer, PM10 concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM10 concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions.

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Section: Resultssupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Motivation and Research Questionsmentioning

confidence: 99%

Section: Modis Ndvimentioning

confidence: 99%

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Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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“…Recently, Chen et al (2017) reported an improvement in the leaf area index (LAI) retrievals with the MODIS LAI/FPAR algorithm when using MAIAC instead of standard MODIS MOD09 input. A high accuracy, high 1 km spatial resolution and high retrieval coverage made MAIAC aerosol optical depth (AOD) a focus of numerous air quality studies, e.g., Chudnovsky et al (2013), Kloog et al (2014), Just et al (2015), Di et al (2016), Stafoggia et al (2016), Tang et al (2017) and Xiao et al (2017) to name a few. Currently published validation studies (Martins et al, 2017;Superczynski et al, 2017) show a high MAIAC AOD accuracy over American continents and an improved accuracy and coverage over North America compared to the operational VIIRS algorithm (Superczynski et al, 2017).…”

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confidence: 99%

Response to Reviewer 2

Lyapustin¹

2018

Preprint

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This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record. Since initial publication in 2011-2012, MA-IAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine-and coarsemode aerosols, (4) adding over-water processing, (5) general optimization of the LUT-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 µm, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

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Clustering of bivariate satellite time series: A quantile approach

2022

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Clustering has received much attention in statistics and machine learning with the aim of developing statistical models and autonomous algorithms which are capable of acquiring information from raw data in order to perform exploratory analysis. Several techniques have been developed to cluster sampled univariate vectors only considering the average value over the whole period and as such they have not been able to explore fully the underlying distribution as well as other features of the data, especially in presence of structured time series. We propose a model‐based clustering technique that is based on quantile regression permitting us to cluster bivariate time series at different quantile levels. We model the within cluster density using asymmetric Laplace distribution allowing us to take into account asymmetry in the distribution of the data. We evaluate the performance of the proposed technique through a simulation study. The method is then applied to cluster time series observed from Glob‐color satellite data related to trophic status indices with aim of evaluating their temporal dynamics in order to identify homogeneous areas, in terms of trophic status, in the Gulf of Gabes.

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Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology

Cited by 114 publications

References 40 publications

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Response to Reviewer 2

Clustering of bivariate satellite time series: A quantile approach

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