Combining land use regression models and fixed site monitoring to reconstruct spatiotemporal variability of NO2 concentrations over a wide geographical area

Cordioli, Michele; Pironi, Claudia; Munari, Eriberto De; Marmiroli, Nelson; Lauriola, Paolo; Ranzi, Andrea

doi:10.1016/j.scitotenv.2016.09.089

Cited by 28 publications

(16 citation statements)

References 34 publications

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“…This study expands the use of data from routine FSMs beyond compliance, or as a means of adjusting and limiting bias in passive sampling campaigns (Beelen et al, 2013;Henderson et al, 2007). The greater temporal coverage and short averaging period of FSM data (Cordioli et al, 2017) allowed us to divide the data wind sectors that, in addition to allowing the isolation of relevant source effects, also reduced site biases and allowed the introduction of a time variant aspect into the model. Furthermore, the reliability of the FSM measurements is typically higher than would be the case for many low-cost passive sampling techniques.…”

Section: Discussionmentioning

confidence: 97%

A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach

Naughton

Donnelly

Nolan

et al. 2018

Science of The Total Environment

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Estimating pollutant concentrations at a local and regional scale is essential in environmental and health policy decision making. Here we present a novel land use regression (LUR) modelling methodology that exploits the high temporal resolution of fixed-site monitoring (FSM) to produce a national-scale air quality model for the key pollutant NO. The methodology partitions concentration time series from a national FSM network into wind-dependent sectors or "wedges". A LUR model is derived using predictor variables calculated within the directional wind sectors, and compared against the long-term average concentrations within each sector. Validation results, based on 15 FSM training sites, show that the model captured 78% of the spatial variability in NO across the Republic of Ireland. This compares favourably to traditional LUR models based on purpose-designed monitoring campaigns despite using approximately half the number of monitoring points. Results also demonstrate the value of incorporating the relative position of emission source and receptor into the empirical LUR model structure. We applied the model at a high-resolution across the Republic of Ireland to enable applications such as the study of environmental exposure and human health, assessing representativeness of air quality monitoring networks and informing environmental management and policy makers. While the study focuses on Ireland, the methodology also has potential applicability for other criteria pollutants where appropriate FSM and meteorological networks exist.

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Section: Discussionmentioning

confidence: 97%

A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach

Naughton

Donnelly

Nolan

et al. 2018

Science of The Total Environment

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“…The selected criteria were: elevation, maximum temperature, minimum temperature, slope angle, rainfall, runoff coefficient, and slope, which were calculated by AHP through its pair-wise comparison matrix procedure. The weights of the criteria are calculated by Equations (6)(7)(8), where the inconsistency index = 0.05; it is desirable to have a value of less than 0.1.…”

Section: Resultsmentioning

confidence: 99%

“…The use of land for agriculture is considered critical areas in the scientific literature [1][2][3][4]. In general, land evaluation is the process of assessing the performance of land when used for a given purpose [5][6][7]. An evaluation of the climate, soil, and topographical components, along with an author's knowledge.…”

Section: Introductionmentioning

confidence: 99%

GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): Toward an Integrated Citrus Management Strategy

et al. 2019

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This study proposes a site location assessment model for citrus cropland using multi-criteria evaluation (MCE) and the combination of a set of factors for suitability mapping and delineating the suitable areas for citrus production in Ramsar, Iran. It defines an incorporated method for the suitability mapping of the most appropriate sites for citrus cultivars with an emphasis on the multi-criteria decision analysis (MCDA) process. The combination of geographic information system (GIS) and a modified version of the analytic hierarchy process (AHP) based on the ordered weighted averaging (OWA) technique is also emphasized. The OWA is based on two principles, namely: the weights of relative criterion significance and the order weights. Therefore, the participatory technique was employed to outline the set of standards and the important criterion. The results derived from the GIS–OWA technique indicate that the cultivation of citrus is feasible only in limited areas, which make up 6.7% of the total area near the Caspian Sea. This investigation has shown that the GIS–OWA model can be integrated into MCDA to select the optimal site for citrus production. The present research highlights how multi-criteria in GIS can play a considerable role in decision making for evaluating the suitability of selected sites for citrus production.

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“…We classified urban and non-urban areas using land cover categories as administrative units. The NO 2 levels in urban regions are more variable than in rural areas due to micro-meteorological factors [36] and a high proportion of artificial surfaces in urban settings. Based on the land cover data, residential, industrial, commercial, recreational, traffic, and public areas were all classified as artificial surfaces.…”

Section: Prediction Of No 2 Concentrations By Administrative Unit Usimentioning

confidence: 99%

“…Previous studies have used spatial data on traffic, population, land use, and physical geography as predictor variables [4,5,[35][36][37][38][39][40][41][42][43]. A total of 18 environmental variables have been used as predictor variables (Table 1).…”

Section: Prediction Of No 2 Concentrations By Administrative Unit Usimentioning

confidence: 99%

Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

2019

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With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO2. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO2 concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO2 concentration data, an administrative unit map was generated. When comparing the data for four NO2 data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO2 concentrations averaged by an administrative unit based on the interpolated NO2 concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO2 concentration by region using NO2 concentrations for the administrative units.

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Combining land use regression models and fixed site monitoring to reconstruct spatiotemporal variability of NO2 concentrations over a wide geographical area

Cited by 28 publications

References 34 publications

A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach

A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach

GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): Toward an Integrated Citrus Management Strategy

Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

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