 Spatial Analysis of Air Pollutants in an Industrial City Using GIS-Based Techniques: A Case Study of Pavlodar, KazakhstanRuslan Safarov,
Zhanat Shomanova (),
Yuriy Nossenko (),
Eldar Kopishev,
Zhuldyz Bexeitova and
Ruslan Kamatov
Sustainability, 2024, vol. 16, issue 17, 1-25 Abstract: The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM 2.5 , PM 10 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ground-level ozone (O 3 ); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM 2.5 and NO 2 as the primary contributors to air pollution in Pavlodar, with NO 2 exceeding the 24 h threshold in 87.38% of locations and PM 2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM 2.5 and AQI and a negative correlation between NO 2 and AQI, likely due to the dominant influence of PM 2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM 2.5 as the most significant impactor on AQI, while NO 2 shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM 2.5 , PM 10 , and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO 2 pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center. Keywords: air pollution; air quality index (AQI); particulate matter (PM 2.5; PM 10 ); green space ratio; road ratio; Pavlodar, Kazakhstan (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:17:p:7834-:d:1474010 Access Statistics for this article Sustainability is currently edited by Ms. Alexandra Wu More articles in Sustainability from MDPI |
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