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Cost Effective Sampling Techniques in Environmental Monitoring
Abstract
Environmental monitoring is vital for assessing ecosystem health and detecting pollution, but traditional methods often require extensive data collection, resulting in high costs. This study proposes a cost-effective sampling approach that compares SRS, stratified, cluster, and adaptive sampling techniques to optimize resource allocation without compromising data quality. The research uses the NLA 2022 dataset from the U.S. Environmental Protection Agency to reduce sampling costs while maintaining accurate water quality assessments. Key parameters such as geographical location, lake size, and urban/rural classification will be used to estimate travel, labor, and analysis costs. The methods used highlight the strengths of stratified sampling to ensure geographic coverage, cluster sampling to reduce travel costs, and adaptive sampling to focus on areas of higher environmental variability. This research provides a practical comparison for environmental agencies to implement more cost-effective monitoring strategies while maintaining high-quality data collection.
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