摘要
以南京市汽油车遥测试验结果为例,对汽油车的单次遥测数据与年检数据进行分析,结果表明,2组数据之间不存在相关性,这可能是由于车速等检测环境的差异造成的。整体遥测值的变化并不会随着年检值的增加或减少而呈现某一趋势,车辆的单次遥测数据并不具有代表性,不能作为车辆是否超标的判定依据。在对汽油车遥测不合格次数、排放限值设定、遥测年检时间间隔等分析中发现,若将汽油车的NO、CO遥测排放限值分别设为2 000×10-6和5%,当汽油车的遥测不合格次数≥5次,或汽油车的遥测不合格次数达到3次,同时这3次的遥测时间距离年检不超过60 d(遥测在前,年检在后),那么在这些遥测不合格车辆中,有80%以上的年检结果也不合格,遥测与年检的检测结果判定较为一致。虽然两者的检测方法和排放标准不同,但都能有效筛选出排放超标车辆,而且遥测法更快速,也无需停车检测,不影响车辆行驶。
We have taken the resiults of the telemetry test of gasoline vehicles in Nanjing as an example,to analyze the single telem-etry test data of the gasoline vehicles and the annual inspection data. I found that there is no correlation between the 2 sets of data. This may be caused by differences in vehicle speed and other detection environments. The changes of overall telemetry value will not present a trend with the increase or decrease of annual check value,the single telemetry data of vehicles is not representative, it cannot be the basis for determining whetlier the vehicle exceeds the standard. In the analysis of the unsatry gasoline vehicles, the setting of emission limits,and the time interval of the telemetry annual inspection,we found that when theunqualified number of a telemetry gasoline vehicle reaches 5 times or more,or the unquaified number of the telemetry vehicle rea-ches 3 times, at the same time, this 3 times happened withiin 60 days between the telemetry and the annual inspection% telemetry is before of the annual inspection), then in this unqualified vehicles, more than 80% of those vehicles ’ annual results are also un-qualified ,the test results of telemetry and annual inspection are more consistent. Although the detection methods and emissionstandards of the 2 methods are different,they can effectively filter out the exccssive emission vehicles,and the telemetry method ismore rapid, and do not need to stop detection,which does not affect the driving of vehicles.
出处
《环境监控与预警》
2018年第1期42-45,共4页
Environmental Monitoring and Forewarning
关键词
机动车尾气
共享检测
汽油车
遥感检测
南京
Automobile emission Shared detection
Gasoline vehicle
Remote sensing detection
Nanjing