No abstract

No abstract

Increasing carbon dioxide (CO2) concentrations threaten human production and life. Currently the equipment used for CO2 monitoring is heavy and expensive, without a portable CO2 detector that is inexpensive and resistant to interference. Here we designed a portable CO2 detector based on no-dispersive infrared sensors to measure CO2 concentration. The detector, which has a mass of 1 kg, is powered by a lithium battery with dimensions of 200 mm (length) × 150 mm (width) × 100 mm (height). Considering the fact that field observations are susceptible to humidity, a series of experiments were carried out to reduce the humidity interference on sensor responses at a laboratory. The values of humidity and CO2 variation were used in a regression model analysis to determine a quadratic function with an R2 above 0.94. The detector was compared with a reference analyzer in ambient CO2 measurement during a 7-day field campaign in Hangzhou, China. After humidity correction, the data show better correlation with the reference data, with the R2 0.62–0.97 increasing from 0.62–0.97 compared to before the correction and the value deviation decreasing to less than 3%. Cluster analysis of sensors revealed a reduction in average relative deviation of up to 1.4% as the number of sensors increased.

Fine chemical industrial park (FCIP) is a major source of atmospheric pollutants in China. A long-term high spatial resolution monitoring campaign on air pollutants had been firstly conducted in a major FCIP in Yangtze River Delta (YRD) from December 2019 to November 2020. The grid-based monitoring platform consisting of 30 miniature air quality monitoring stations (AQMSs) provided comprehensive coverage of a FCIP, and long-term monitoring studies solved the problem of lack of clarity about pollution sources in industrial parks. Overall, NO2 pollution was particularly high in the pharmaceutical industry, while TVOCs and O3 pollution were most serious in the textile dyeing industry, with PM pollution much higher in the metal smelting industry than in other industries, and in the leather industry, O3 pollution was relatively severe. The spatial and temporal variations of air pollutants showed that higher PM, CO and NO2 concentrations were revealed in winter while lower in summer due to better meteorological diffusion conditions. TVOCs concentrations were higher with an average of 1954 ppb in summer possibly due to their increased volatilization from their sources at higher ambient temperatures. O3 concentrations were at their peaks in spring (88.8 μg m−3) and early fall (78.5 μg m−3). The daily trends of O3 precursors (TVOCs and NO2) were clearly negatively correlated with O3, and they showed bimodal peaks due to anthropogenic activities, plant emissions, lowering of the mixed boundary layer, etc. The O3 formed in FCIP was judged to be NO2-limited during the monitoring period based on the ratios of NO2 to TVOCs. Therefore, the effective strategy to reduce O3 formation in FCIP is to decrease the ambient NO2 concentration. Based on Pearson correlation coefficients, it appeared that WS promoted O3 formation through long-term transport and that high air temperatures also contributed to O3 formation in the environment. It was also stated in the study that the closer the residential area is to the industrial sources, the more significant the correlation. Thus, the results of this study will also be helpful for policymakers to design pollutant control strategies for different industries to mitigate the impact of pollutants on human health.

Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel.