Supplementary material to "The Berkeley Environmental Air-quality and CO<sub>2</sub> Network: field calibrations of sensor temperature dependence and assessment of network scale CO<sub>2</sub> accuracy"

2021 ◽  
Author(s):  
Erin Rose Delaria ◽  
Jinsol Kim ◽  
Helen L. Fitzmaurice ◽  
Catherine Newman ◽  
Paul J. Wooldridge ◽  
...  
Keyword(s):  
Air Quality ◽  
Temperature Dependence ◽  
Network Scale ◽  
Supplementary Material ◽  
Sensor Temperature ◽  
Environmental Air

scholarly journals The Berkeley Environmental Air-quality and CO<sub>2</sub> Network: field calibrations of sensor temperature dependence and assessment of network scale CO<sub>2</sub> accuracy

2021 ◽  
Author(s):  
Erin Rose Delaria ◽  
Jinsol Kim ◽  
Helen L. Fitzmaurice ◽  
Catherine Newman ◽  
Paul J. Wooldridge ◽  
...  
Keyword(s):  
Air Quality ◽  
Temperature Dependence ◽  
Co2 Emissions ◽  
Total Error ◽  
Temperature Dependent ◽  
Dense Networks ◽  
Sensor Temperature ◽  
Zero Offset ◽  
Environmental Air ◽  
Point Line

Abstract. The majority of global CO2 emissions originate in cities. We have proposed that dense networks are a strategy for tracking changes to the processes contributing to urban CO2 emissions and suggested that a network with ∼2 km measurement spacing and ∼1 ppm node-to-node precision would be effective at constraining point, line and area sources within cities. Here we report on an assessment of the accuracy of the Berkeley Environmental Air-quality and CO2 Network (BEACO2N) CO2 measurements over several years of deployment. We describe a new procedure for improving network accuracy that accounts for and corrects the temperature dependent zero offset of the Vaisala CarboCap GMP343 CO2 sensors used. With this correction we show that a total error of 1.6 ppm or less can be achieved for networks that have a calibrated reference location and 3.6 ppm for networks without a calibrated reference.

scholarly journals The Berkeley Environmental Air-quality and CO<sub>2</sub> Network: field calibrations of sensor temperature dependence and assessment of network scale CO<sub>2</sub> accuracy

2021 ◽  
Vol 14 (8) ◽  
pp. 5487-5500
Author(s):  
Erin R. Delaria ◽  
Jinsol Kim ◽  
Helen L. Fitzmaurice ◽  
Catherine Newman ◽  
Paul J. Wooldridge ◽  
...  
Keyword(s):  
Air Quality ◽  
Temperature Dependence ◽  
Co2 Emissions ◽  
Total Error ◽  
Temperature Dependent ◽  
Dense Networks ◽  
Sensor Temperature ◽  
Zero Offset ◽  
Environmental Air ◽  
Point Line

Abstract. The majority of global anthropogenic CO2 emissions originate in cities. We have proposed that dense networks are a strategy for tracking changes to the processes contributing to urban CO2 emissions and suggested that a network with ∼ 2 km measurement spacing and ∼ 1 ppm node-to-node precision would be effective at constraining point, line, and area sources within cities. Here, we report on an assessment of the accuracy of the Berkeley Environmental Air-quality and CO2 Network (BEACO2N) CO2 measurements over several years of deployment. We describe a new procedure for improving network accuracy that accounts for and corrects the temperature-dependent zero offset of the Vaisala CarboCap GMP343 CO2 sensors used. With this correction we show that a total error of 1.6 ppm or less can be achieved for networks that have a calibrated reference location and 3.6 ppm for networks without a calibrated reference.

scholarly journals Spatial and Temporal Characteristics of Environmental Air Quality and Its Relationship with Seasonal Climatic Conditions in Eastern China during 2015–2018

2021 ◽  
Vol 18 (9) ◽  
pp. 4524
Author(s):  
Zhiyuan Wang ◽  
Xiaoyi Shi ◽  
Chunhua Pan ◽  
Sisi Wang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Large Scale ◽  
Asian Summer Monsoon ◽  
Eastern China ◽  
Climatic Conditions ◽  
Uniform Structure ◽  
Concentration Limit ◽  
Environmental Air

Exploring the relationship between environmental air quality (EAQ) and climatic conditions on a large scale can help better understand the main distribution characteristics and the mechanisms of EAQ in China, which is significant for the implementation of policies of joint prevention and control of regional air pollution. In this study, we used the concentrations of six conventional air pollutants, i.e., carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3), derived from about 1300 monitoring sites in eastern China (EC) from January 2015 to December 2018. Exploiting the grading concentration limit (GB3095-2012) of various pollutants in China, we also calculated the monthly average air quality index (AQI) in EC. The results show that, generally, the EAQ has improved in all seasons in EC from 2015 to 2018. In particular, the concentrations of conventional air pollutants, such as CO, SO2, and NO2, have been decreasing year by year. However, the concentrations of particulate matter, such as PM2.5 and PM10, have changed little, and the O3 concentration increased from 2015 to 2018. Empirical mode decomposition (EOF) was used to analyze the major patterns of AQI in EC. The first mode (EOF1) was characterized by a uniform structure in AQI over EC. These phenomena are due to the precipitation variability associated with the East Asian summer monsoon (EASM), referred to as the “summer–winter” pattern. The second EOF mode (EOF2) showed that the AQI over EC is a north–south dipole pattern, which is bound by the Qinling Mountains and Huaihe River (about 35° N). The EOF2 is mainly caused by seasonal variations of the mixed concentration of PM2.5 and O3. Associated with EOF2, the Mongolia–Siberian High influences the AQI variation over northern EC by dominating the low-level winds (10 m and 850 hPa) in autumn and winter, and precipitation affects the AQI variation over southern EC in spring and summer.

scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

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