CNN Algorithm for Improving the Performance of Pollutant Identification in Environmental Monitoring Images

In the context of rapid technological progress and urban growth, environmental pollution problems have become more complex. Traditional manual detection methods are no longer sufficient to meet the current requirements for precision and operation efficiency. To overcome the limitations of old—fashioned pollutant classification methods in environmental monitoring, which are characterized by low performance and high error rates, this study presents an improved system that makes use of CNN architectures. The proposed methodology increases the extraction of discriminative features and optimizes computational efficiency. This is achieved by carefully modifying the sizes of filters in convolutional operations and expanding the hierarchical structures of the network. Additionally, the training process is systematically improved. Dimensionality reduction is carried out through principal component decomposition, which transforms multi—dimensional image features into a more concise coordinate system. This procedure selectively retains the features that are crucial for pollutant discrimination, thus speeding up parameter convergence and enhancing the accuracy of classification. Experimental verification shows that the re—structured neural network achieves an average comprehensive detection rate of 91.5% for various pollutant categories. This represents an 18.7% improvement compared to traditional methods. In practical field applications, when combined with parameter quantization strategies, the architecture is compatible with distributed edge computing models, enabling resource—efficient real—time monitoring. This new paradigm promotes intelligent environmental monitoring, providing environmental management with data—based analytical tools for targeted actions and sustainable governance.