Intelligent modeling strategies for forecasting air quality time series: A review

2021 ◽  
Vol 102 ◽  
pp. 106957
Author(s):  
Hui Liu ◽  
Guangxi Yan ◽  
Zhu Duan ◽  
Chao Chen
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Intelligent Modeling

scholarly journals Urban influence on the concentration and composition of submicron particulate matter in central Amazonia

2018 ◽  
Author(s):  
Suzane S. de Sá ◽  
Brett B. Palm ◽  
Pedro Campuzano-Jost ◽  
Douglas A. Day ◽  
Weiwei Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Air Quality ◽  
Nitrogen Oxides ◽  
Mass Concentration ◽  
Critical Role ◽  
Metropolitan Region ◽  
Anthropogenic Emissions ◽  
Organic Mass ◽  
The City

Abstract. Fundamental to quantifying the influence of human activities on climate and air quality is an understanding of how anthropogenic emissions affect the concentrations and composition of airborne particulate matter (PM). The central Amazon basin, especially around the city of Manaus, Brazil, has experienced rapid changes in the past decades due to ongoing urbanization. Herein, changes in the concentration and composition of submicron PM due to pollution downwind of the Manaus metropolitan region are reported as part of the GoAmazon2014/5 experiment. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a suite of other gas- and particle-phase instruments were deployed at the T3 research site, 70 km downwind of Manaus, during the wet season. At this site, organic components represented on average 79 ± 7 % of the non-refractory PM1 mass concentration, which was in the same range as several upwind sites. The organic PM1 was, however, considerably more oxidized at T3 compared to upwind measurements. Positive-matrix factorization (PMF) was applied to the time series of organic mass spectra collected at the T3 site, yielding three factors representing secondary processes (73 ± 15 % of total organic mass concentration) and three factors representing primary anthropogenic emissions (27 ± 15 %). Fuzzy c-means clustering (FCM) was applied to the afternoon time series of concentrations of NOy, ozone, total particle number, black carbon, and sulfate. Four clusters were identified and characterized by distinct airmass origins and particle compositions. Two clusters, Bkgd-1 and Bkgd-2, were associated with background conditions. Bkgd-1 appeared to represent near-field atmospheric PM production and oxidation of a day or less. Bkgd-2 appeared to represent material transported and oxidized for two or more days, often with out-of-basin contributions. Two other clusters, Pol-1 and Pol-2, represented the Manaus influence, one apparently associated with the northern region of Manaus and the other with the southern region of the city. A composite of the PMF and FCM analyses provided insights into the anthropogenic effects on PM concentration and composition. The increase in mass concentration of submicron PM ranged from 25 % to 200 % under polluted compared to background conditions, including contributions from both primary and secondary PM. Furthermore, a comparison of PMF factor loadings for different clusters suggested a shift in the pathways of PM production under polluted conditions. Nitrogen oxides may have played a critical role in these shifts. Increased concentrations of nitrogen oxides can shift pathways of PM production from HO2-dominant to NO-dominant as well as increase the concentrations of oxidants in the atmosphere. Consequently, the oxidation of biogenic and anthropogenic precursor gases as well as the oxidative processing of pre-existing atmospheric PM can be accelerated. The combined set of results demonstrates the susceptibility of atmospheric chemistry, air quality, and associated climate forcing to anthropogenic perturbations over tropical forests.

scholarly journals Turbulent transport, emissions and the role of compensating errors in chemical transport models

2014 ◽  
Vol 7 (3) ◽  
pp. 1001-1024 ◽  
Author(s):  
P. A. Makar ◽  
R. Nissen ◽  
A. Teakles ◽  
J. Zhang ◽  
Q. Zheng ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Air Quality ◽  
Lower Limit ◽  
Turbulent Mixing ◽  
Turbulent Transport ◽  
Model Error ◽  
Quality Models ◽  
Regional Air Quality ◽  
Pm2.5 Emissions

Abstract. The balance between turbulent transport and emissions is a key issue in understanding the formation of O3 and particulate matter with diameters less than 2.5 μm (PM2.5). Discrepancies between observed and simulated concentrations for these species have, in the past, been ascribed to insufficient turbulent mixing, particularly for atmospherically stable environments. This assumption may be simplistic – turbulent mixing deficiencies may explain only part of these discrepancies, and as turbulence parameterizations are improved, the timing of primary PM2.5 emissions may play a much more significant role in the further reduction of model error. In a study of these issues, two regional air-quality models, the Community Multi-scale Air Quality model (CMAQ, version 4.6) and A Unified Regional Air-quality Modelling System (AURAMS, version 1.4.2), were compared to observations for a domain in north-western North America. The air-quality models made use of the same emissions inventory, emissions processing system, meteorological driving model, and model domain, map projection and horizontal grid, eliminating these factors as potential sources of discrepancies between model predictions. The initial statistical comparison between the models and monitoring network data showed that AURAMS' O3 simulations outperformed those of this version of CMAQ4.6, while CMAQ4.6 outperformed AURAMS for most PM2.5 statistical measures. A process analysis of the models revealed that many of the differences between the models' results could be attributed to the strength of turbulent diffusion, via the choice of an a priori lower limit in the magnitude of vertical diffusion coefficients, with AURAMS using 0.1 m2 s−1 and CMAQ4.6 using 1.0 m2 s−1. The use of the larger CMAQ4.6 value for the lower limit of vertical diffusivity within AURAMS resulted in a similar performance for the two models (with AURAMS also showing improved PM2.5, yet degraded O3, and a similar time series as CMAQ4.6). The differences between model results were most noticeable at night, when the higher minimum turbulent diffusivity resulted in an erroneous secondary peak in predicted night-time O3. A spatially invariant and relatively high lower limit in diffusivity could not reduce errors in both O3 and PM2.5 fields, implying that other factors aside from the strength of turbulence might be responsible for the PM2.5 over-predictions. Further investigation showed that the magnitude, timing and spatial allocation of area source emissions could result in improvements to PM2.5 performance with minimal O3 performance degradation. AURAMS was then used to investigate a land-use-dependant lower limit in diffusivity of 1.0 m2 s−1 in urban regions, linearly scaling to 0.01 m2s−1 in rural areas, as employed in CMAQ5.0.1. This strategy was found to significantly improve mean statistics for PM2.5 throughout the day and mean O3 statistics at night, while significantly degrading (halving) midday PM2.5 correlation coefficients and slope of observed to model simulations. Time series of domain-wide model error statistics aggregated by local hour were shown to be a useful tool for performance analysis, with significant variations in performance occurring at different hours of the day. The use of the land-use-dependant lower limit in diffusivity was also shown to reduce the model's sensitivity to the temporal allocation of its emissions inputs. The modelling scenarios suggest that while turbulence plays a key role in O3 and PM2.5 formation in urban regions, and in their downwind transport, the spatial and temporal allocation of primary PM2.5 emissions also has a potentially significant impact on PM2.5 concentration levels. The results show the complex nature of the interactions between turbulence and emissions, and the potential of the strength of the former to mask the impact of changes in the latter.

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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