地下停车场车辆尾气排放污染物的预测与评价

随着机动车数量不断增长,车辆尾气排放对人们的影响也在加剧,本文以沈阳市某住宅小区地下停车场的车辆尾气排放实测数据与环评工作中常用的经验预测公式进行比较,分析计算尾气污染物的各预测公式的准确程度;同时,对预测值进行评价时,采用不同空气质量标准得出的达标结论有所不同,以此比较不同标准的适用性,为该类项目的环评提供参考。     

辐射环境监测数据合理性评价中的问题探讨

对辐射环境监测数据合理性评价中探测限附近的数据、监测结果趋势的分析和实验室分析过程控制中的相关问题进行分析和探讨,提出了意见和建议。     

How to evaluate models: Observed vs. predicted or predicted vs. observed?

A common and simple approach to evaluate models is to regress predicted vs. observed values (or vice versa) and compare slope and intercept parameters against the 1:1 line. However, based on a review of the literature it seems to be no consensus on which variable (predicted or observed) should be placed in each axis. Although some researchers think that it is identical, probably because r² is the same for both regressions, the intercept and the slope of each regression differ and, in turn, may change the result of the model evaluation. We present mathematical evidence showing that the regression of predicted (in the y-axis) vs. observed data (in the x-axis) (PO) to evaluate models is incorrect and should lead to an erroneous estimate of the slope and intercept. In other words, a spurious effect is added to the regression parameters when regressing PO values and comparing them against the 1:1 line. Observed (in the y-axis) vs. predicted (in the x-axis) (OP) regressions should be used instead. We also show in an example from the literature that both approaches produce significantly different results that may change the conclusions of the model evaluation.