In order to improve the level of precision in air pollution prevention and control, Jinan City recently issued the "Jinan Road Particulate Matter Assessment Measures (Trial)" (hereinafter referred to as the "Measures"), which will fully utilize the road navigation monitoring system independently developed by Shandong Nova Technology Co., Ltd., and innovate the trial of quantitative evaluation and assessment of particulate matter pollution status.

        This project utilizes a high-precision vehicle mounted atmospheric particulate matter monitoring system developed by Shandong University and Shandong Nova after two years of research and development. The instrument equipment is based on the principle of laser detection and installed on the roof lights of taxis, overcoming the adverse environmental effects of high temperature, high speed, vibration, wind disturbance, rain and snow. It can simultaneously monitor PM_2.5 and PM₁₀ indicators, transmit location and monitoring data in real time, and successfully achieve the transformation from fixed point monitoring to whole road network monitoring. It has opened up new ideas for atmospheric pollution monitoring and made taxis a new platform for atmospheric monitoring. So far, 10 invention patents have been applied for.

        At present, a total of 300 vehicles have been installed in the first and second phases of Jinan City, with a total daily travel of over 69000 kilometers and the ability to generate 3.6 million sets of data, covering 98% of the roads in the urban area. With the help of a smart environmental big data monitoring platform, these data can automatically generate a detailed urban haze map.

        By comparing and analyzing data from national and provincial air quality monitoring points, technicians can further determine whether pollution source supervision in relevant areas is in place, guide precise governance, and rank counties, street offices, and road sections in statistics, providing technical means for governance assessment. In addition, using taxi mobile platforms for atmospheric monitoring requires almost no additional manpower or resources, has low operating costs, and is low-carbon and energy-saving.

        The Measures comprehensively use road navigation PM₁₀, PM_2.5 monitoring data and main road PM₁₀ online monitoring data for assessment, with Lixia District, Shizhong District, Huaiyin District, Tianqiao District, Licheng District Government and High tech Zone Management Committee as the assessment objects. It is reported that according to road navigation monitoring data, Jinan City classifies particulate matter pollution on road sections into red, orange, yellow, and green categories by hour, day, and month. Red is the worst and green is the best, and it is publicly available in real-time through the Jinan Environment APP.

        The Measures also clarify the assessment and ranking methods, as well as the rules for financial rewards and punishments. Based on the monitoring data of PM₁₀ and PM_2.5 on the assessed road sections in each district, as well as the monthly average concentration status and month on month improvement of PM₁₀ online monitoring data on main roads, a percentage scoring ranking system will be implemented. Calculate the amount of withholding and reward funds based on the length of red, orange, yellow, and green road sections in each district and the improvement results of road particulate matter every month. The highest standard for fund withholding is to withhold 6000 yuan/kilometer/month and 4000 yuan/kilometer/month respectively for PM₁₀ and PM_2.5 red road sections; The minimum deduction for PM₁₀ and PM_2.5 on yellow road sections is 1200 yuan/kilometer/month and 800 yuan/kilometer/month, respectively. 25%, 15%, and 10% of the withheld funds for the current month will be used to reward and improve the top three districts respectively; 50% of the withheld funds will be used to reward green road sections for the current month. The municipal finance department will settle funds in relevant areas every quarter based on the results of fund rewards and punishments.