<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "https://jats.nlm.nih.gov/nlm-dtd/publishing/3.0/journalpublishing3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="3.0" xml:lang="en">
<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprsarchives-XL-8-205-2014</article-id>
<title-group>
<article-title>Geomatics Approach for Assessment of respiratory disease Mapping</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Pandey</surname>
<given-names>M.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Singh</surname>
<given-names>V.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Vaishya</surname>
<given-names>R. C.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>GIS Cell,Motilal Nehru National Institute of Technology, Allahabad, India</addr-line>
</aff>
<pub-date pub-type="epub">
<day>27</day>
<month>11</month>
<year>2014</year>
</pub-date>
<volume>XL-8</volume>
<fpage>205</fpage>
<lpage>211</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2014 M. Pandey et al.</copyright-statement>
<copyright-year>2014</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XL-8/205/2014/isprs-archives-XL-8-205-2014.html">This article is available from https://isprs-archives.copernicus.org/articles/XL-8/205/2014/isprs-archives-XL-8-205-2014.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XL-8/205/2014/isprs-archives-XL-8-205-2014.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XL-8/205/2014/isprs-archives-XL-8-205-2014.pdf</self-uri>
<abstract>
<p>Air quality is an important subject of relevance in the context of present times because air is the prime resource for sustenance of life
especially human health position. Then with the aid of vast sums of data about ambient air quality is generated to know the character
of air environment by utilizing technological advancements to know how well or bad the air is. This report supplies a reliable
method in assessing the Air Quality Index (AQI) by using fuzzy logic. The fuzzy logic model is designed to predict Air Quality
Index (AQI) that report monthly air qualities. With the aid of air quality index we can evaluate the condition of the environment of
that area suitability regarding human health position. For appraisal of human health status in industrial area, utilizing information
from health survey questionnaire for obtaining a respiratory risk map by applying IDW and Gettis Statistical Techniques. Gettis
Statistical Techniques identifies different spatial clustering patterns like hot spots, high risk and cold spots over the entire work area
with statistical significance.</p>
</abstract>
<counts><page-count count="7"/></counts>
</article-meta>
</front>
<body/>
<back>
</back>
</article>