A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models

<div><p>In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O₃) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs’ objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O₃ was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O₃, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O₃ monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the performance of air quality monitoring networks in urban regions.</p><p>Implications:<i>The U.S. Environmental Protection Agency issued a directive in 2006 that air pollution agencies assess the performance of their AQMNs; as a result, we developed a GIS-based, multi-objective assessment approach that integrates environmental, economic, and social indicators, and demonstrates its use through assessing the O₃ and PM₁₀ monitoring networks in the Phoenix metropolitan area. We exhibit a method of assessing network performance and identifying areas that would benefit from new monitoring stations; also, we demonstrate the effect of adding weights to the indicators. Our study shows that using a multi-indicator approach gave detailed assessment results for the Phoenix AQMN.</i></p></div

Different forms of the relationship between habitat loss and habitat fragmentation, representing different hypotheses reported in the literature (See S1 Appendix for the detailed sources).

<p>*Landscape metrics include: (1) area metrics, i.e., mean patch size (MPS), total core area (TCA), and normalized TCA (NTCA); (2) density metrics, i.e., patch density (PD) and edge density (ED); (3) shape metrics, i.e., landscape shape index (LSI) and perimeter-area fractal dimension (PAFD); and (4) connectivity metrics, i.e., mean Euclidean nearest neighbor distance (NND), normalized NND (NNND), and Cohesion (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.t001" target="_blank">Table 1</a> for details).</p

List of landscape metrics used in the study, all of which, except normalized total core area and normalized nearest neighbor distance, were based on McGarigal et al. [32] and Wu et al. [33].

<p>List of landscape metrics used in the study, all of which, except normalized total core area and normalized nearest neighbor distance, were based on McGarigal et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.ref032" target="_blank">32</a>] and Wu et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.ref033" target="_blank">33</a>].</p

Determination of volatile organic compounds - BTEX in soil and sediments

<p><b>The locations of the 16 study cities (a) and, as an example, the habitat loss during urbanization in Paris from 1800 to 2000 (b).</b></p

Spatial patterns of habitat and corresponding values of landscape metrics with decreasing percentages of habitat in Paris, as an example, derived from both historical landscape pattern analysis and space-for-time analysis.

<p>*The habitats in the historical analysis were derived from data at the central city area extent in Paris in 1800, 1880, 1928, 1955, and 1987. The habitats in the space-for-time analysis were derived from data in Paris in 2000 with the extent of 64 by 64 pixels.</p

Different forms of the relationship between habitat loss and habitat fragmentation during urbanization, derived from historical landscape pattern analysis (See Figs A–J in S4 Appendix for details).

<p>*Landscape metrics include: (1) area metrics, i.e., mean patch size (MPS), total core area (TCA), and normalized TCA (NTCA); (2) density metrics, i.e., patch density (PD) and edge density (ED); (3) shape metrics, i.e., landscape shape index (LSI) and perimeter-area fractal dimension (PAFD); and (4) connectivity metrics, i.e., mean Euclidean nearest neighbor distance (NND), normalized NND (NNND), and Cohesion (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.t001" target="_blank">Table 1</a> for details).</p

The Relationship between Habitat Loss and Fragmentation during Urbanization: An Empirical Evaluation from 16 World Cities - Fig 2

<p><b>Changes in built-up area and habitat in 16 study cities from 1800 to 2000 at two extents: (a) the urban region and (b) the central city area.</b> The study cities are ordered by urban population in 2000.</p

Different forms of the relationship between habitat loss and habitat fragmentation during urbanization in Paris, as an example, derived from historical landscape pattern analysis at the urban regional extent (Number of samples = 10) (See Figs A–J in S4 Appendix for details).

<p>Different forms of the relationship between habitat loss and habitat fragmentation during urbanization in Paris, as an example, derived from historical landscape pattern analysis at the urban regional extent (Number of samples = 10) (See Figs A–J in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.s004" target="_blank">S4 Appendix</a> for details).</p

Different forms of the relationship between habitat loss and habitat fragmentation during urbanization, derived from space-for-time analysis (See Figs K–AD in S4 Appendix for details).

<p>*Landscape metrics include: (1) area metrics, i.e., mean patch size (MPS), total core area (TCA), and normalized TCA (NTCA); (2) density metrics, i.e., patch density (PD) and edge density (ED); (3) shape metrics, i.e., landscape shape index (LSI) and perimeter-area fractal dimension (PAFD); and (4) connectivity metrics, i.e., mean Euclidean nearest neighbor distance (NND), normalized NND (NNND), and Cohesion (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.t001" target="_blank">Table 1</a> for details).</p

Different forms of the relationship between habitat loss and habitat fragmentation during urbanization in Paris, as an example, derived from space-for-time analysis at the extent of 64 by 64 pixels (Number of samples = 818) (See Figs K–T in S4 Appendix for details).

<p>Different forms of the relationship between habitat loss and habitat fragmentation during urbanization in Paris, as an example, derived from space-for-time analysis at the extent of 64 by 64 pixels (Number of samples = 818) (See Figs K–T in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154613#pone.0154613.s004" target="_blank">S4 Appendix</a> for details).</p