Low-cost sensors (LCS) can construct a high spatial and temporal resolution PM_2.5 network but are affected by environmental parameters such as relative humidity and temperature. The data generated by LCS are inaccurate and require calibration against a reference instrument. This study has applied nine machine learning (ML) regression algorithms for Plantower PMS 5003 LCS calibration and compared their performance. The nine ML algorithms applied in this study are: (a) Multiple Linear Regression (MLR); (b) Lasso regression (L1); (c) Ridge regression (L2); (d) Support Vector Regression (SVR); (e) k- Nearest Neighbour (kNN); (f) Multilayer Perceptron (MLP); (g) Regression Tree (RT); (h) Random Forest (RF); (i) Gradient Boosting (GB). The comparison exhibits that kNN, RF and GB have the best performance out of all the algorithms with train scores of 0.99 and test scores of 0.97, 0.96 and 0.95 respectively. This study validates the capability of ML algorithms for the calibration of LCS.

低成本传感器（LCS）可以构建高空间和时间分辨率PM _2.5网络，但受到诸如相对湿度和温度等环境参数的影响。LCS生成的数据不准确，需要根据参考仪器进行校准。这项研究对Plantower PMS 5003 LCS校准应用了九种机器学习（ML）回归算法，并比较了它们的性能。在这项研究中应用的九种机器学习算法是：（a）多元线性回归（MLR）；（b）套索回归（L1）；（c）岭回归（L2）；（d）支持向量回归（SVR）；（e）k-最近邻居（kNN）；（f）多层感知器（MLP）；（g）回归树；（h）随机森林；（i）梯度提升（GB）。比较表明，在所有算法中，kNN，RF和GB的性能最佳，其训练得分分别为0.99和测试得分分别为0.97、0.96和0.95。