EIA of Quarry Impact (Land Loss & Air Quality) in Akure, Nigeria

ID: 1667

Presenting Author: EZEKIEL OGUNGBEMI

Session: 750 - Bridging the Gap: Empowering Local Governments in Developing Economies to Make Environmental Impact Assessments Understandable and Actionable

Status: pending


Summary Statement

This study integrates RS/GIS and EIA, found significant landscape degradation and high human exposure resulting from quarrying in Akure North, Nigeria, despite air quality meeting WHO limits.


Abstract

Quarrying drives infrastructure and economic growth but poses significant environmental challenges in urbanizing areas like Akure North, Ondo State, Nigeria. This study conducted an Environmental Impact Assessment (EIA) focusing on air quality and quarry site expansion by integrating Remote Sensing and GIS techniques. Multi-temporal satellite imagery (2014–2024) from Landsat 8/9 and Google Earth Pro was processed via GEE for LULC classification and change detection across three quarry sites. Air quality data for PM2.5 and PM10 (2021–2024) was obtained from the OpenWeatherMap API and analyzed using Empirical Bayesian Kriging interpolation in ArcGIS Pro. LULC results revealed significant landscape transformation including vegetation cover declination by 67.34%, bareland expansion by 102.73%, and water body surge by 4150% due to quarry-induced craters. Environmental Indices confirmed degradation: the NDVI decreased (0.064–0.408 to 0.030–0.406), confirming vegetation stress. Land Surface Temperature (LST) increased from 29.23–40.72oC to 31.12–42.62 oC, indicating thermal stress. Proximity analysis showed high human exposure: 942 buildings were identified within the 1500m buffer, with the 500m zone experiencing an 87.27% vegetation decline and a 171.89% bareland increase. Air quality demonstrated compliance with WHO guidelines: PM2.5 ranged from 4.91–10.34ug/m3 and PM10 from 10.24–17.03ug/m3, remaining consistently below their respective WHO annual limits (10ug/m3 and 20ug/m3). Particulate matter showed a perfect correlation with PM10 (r= 1.00) and negative correlations with pre


Author Bio

Geospatial Analyst & Data Scientist (BTech. Remote Sensing & GIS) with 4+ years of experience in spatial analysis. Expertise in python programing language for data preparation and analysis.


Coauthor 1: SULEIMAN ADEGBOYEGA

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