Statistical models for multi-step-ahead forecasting of fine particulate matter in urban areas

2019 ◽  
Vol 10 (3) ◽  
pp. 689-700 ◽  
Author(s):  
Ida Kalate Ahani ◽  
Majid Salari ◽  
Alireza Shadman
Keyword(s):  
Particulate Matter ◽  
Statistical Models ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Fine Particulate

scholarly journals Impacts of East Mediterranean megacity emissions on air quality

2012 ◽  
Vol 12 (14) ◽  
pp. 6335-6355 ◽  
Author(s):  
U. Im ◽  
M. Kanakidou
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Biogenic Emissions ◽  
Anthropogenic Emissions ◽  
East Mediterranean ◽  
Fine Particulate ◽  
Surface O3

Abstract. Megacities are large urban agglomerations with intensive anthropogenic emissions that have significant impacts on local and regional air quality. In the present mesoscale modeling study, the impacts of anthropogenic emissions from the Greater Istanbul Area (GIA) and the Greater Athens Area (GAA) on the air quality in GIA, GAA and the entire East Mediterranean are quantified for typical wintertime (December 2008) and summertime (July 2008) conditions. They are compared to those of the regional anthropogenic and biogenic emissions that are also calculated. Finally, the efficiency of potential country-based emissions mitigation in improving air quality is investigated. The results show that relative contributions from both cities to surface ozone (O3) and aerosol levels in the cities' extended areas are generally higher in winter than in summer. Anthropogenic emissions from GIA depress surface O3 in the GIA by ~ 60% in winter and ~ 20% in summer while those from GAA reduce the surface O3 in the GAA by 30% in winter and by 8% in summer. GIA and GAA anthropogenic emissions contribute to the fine particulate matter (PM2.5) levels inside the cities themselves by up to 75% in winter and by 50% (GIA) and ~ 40% (GAA), in summer. GIA anthropogenic emissions have larger impacts on the domain-mean surface O3 (up to 1%) and PM2.5 (4%) levels compared to GAA anthropogenic emissions (<1% for O3 and ≤2% for PM2.5) in both seasons. Impacts of regional anthropogenic emissions on the domain-mean surface pollutant levels (up to 17% for summertime O3 and 52% for wintertime fine particulate matter, PM2.5) are much higher than those from Istanbul and Athens together (~ 1% for O3 and ~ 6% for PM2.5, respectively). Regional biogenic emissions are found to limit the production of secondary inorganic aerosol species in summer up to 13% (non-sea-salt sulfate (nss-SO42−) in rural Athens) due to their impact on oxidant levels while they have negligible impact in winter. Finally, the responses to country-based anthropogenic emission mitigation scenarios inside the studied region show increases in O3 mixing ratios in the urban areas of GIA and GAA, higher in winter (~ 13% for GIA and 2% for GAA) than in summer (~ 7% for GIA and <1% for GAA). On the opposite PM2.5 concentrations decrease by up to 30% in GIA and by 20% in GAA with the highest improvements computed for winter. The emission reduction strategy also leads to domain-wide decreases in most primary pollutants like carbon monoxide (CO) or nitrogen oxides (NOx) for both seasons. The results show the importance of long range transport of pollutants for the air quality in the East Mediterranean. Thus, improvements of air quality in the East Mediterranean require coordinated efforts inside the region and beyond.

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

2015 ◽  
Vol 2503 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Zhanyong Wang ◽  
Hong-Di He ◽  
Feng Lu ◽  
Qing-Chang Lu ◽  
Zhong-Ren Peng
Keyword(s):  
Neural Network ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Air Quality ◽  
Hybrid Model ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Fine Particulate ◽  
Proposed Model

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

scholarly journals Assessment of Population Exposure to Coarse and Fine Particulate Matter in the Urban Areas of Chennai, India

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ramachandran Prasannavenkatesh ◽  
Ramachandran Andimuthu ◽  
Palanivelu Kandasamy ◽  
Geetha Rajadurai ◽  
Divya Subash Kumar ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Urban Areas ◽  
Respiratory Health ◽  
Fine Particulate Matter ◽  
Population Data ◽  
Epidemiological Studies ◽  
World Health ◽  
Population Exposure ◽  
Fine Particulates ◽  
Fine Particulate

Research outcomes from the epidemiological studies have found that the course (PM10) and the fine particulate matter (PM2.5) are mainly responsible for various respiratory health effects for humans. The population-weighted exposure assessment is used as a vital decision-making tool to analyze the vulnerable areas where the population is exposed to critical concentrations of pollutants. Systemic sampling was carried out at strategic locations of Chennai to estimate the various concentration levels of particulate pollution during November 2013–January 2014. The concentration of the pollutants was classified based on the World Health Organization interim target (IT) guidelines. Using geospatial information systems the pollution and the high-resolution population data were interpolated to study the extent of the pollutants at the urban scale. The results show that approximately 28% of the population resides in vulnerable locations where the coarse particulate matter exceeds the prescribed standards. Alarmingly, the results of the analysis of fine particulates show that about 94% of the inhabitants live in critical areas where the concentration of the fine particulates exceeds the IT guidelines. Results based on human exposure analysis show the vulnerability is more towards the zones which are surrounded by prominent sources of pollution.

scholarly journals Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

2021 ◽  
Vol 14 (7) ◽  
pp. 4805-4827
Author(s):  
Amir Yazdani ◽  
Ann M. Dillner ◽  
Satoshi Takahama
Keyword(s):  
Molecular Weight ◽  
Infrared Spectroscopy ◽  
Particulate Matter ◽  
Statistical Models ◽  
Fine Particulate Matter ◽  
Carbon Number ◽  
Multivariate Statistical ◽  
Mid Infrared ◽  
Fine Particulate ◽  
Absorbance Profile

Abstract. Organic matter (OM) is a major constituent of fine particulate matter, which contributes significantly to degradation of visibility and radiative forcing, and causes adverse health effects. However, due to its sheer compositional complexity, OM is difficult to characterize in its entirety. Mid-infrared spectroscopy has previously proven useful in the study of OM by providing extensive information about functional group composition with high mass recovery. Herein, we introduce a new method for obtaining additional characteristics such as mean carbon number and molecular weight of these complex organic mixtures using the aliphatic C−H absorbance profile in the mid-infrared spectrum. We apply this technique to spectra acquired non-destructively from Teflon filters used for fine particulate matter quantification at selected sites of the Inter-agency Monitoring of PROtected Visual Environments (IMPROVE) network. Since carbon number and molecular weight are important characteristics used by recent conceptual models to describe evolution in OM composition, this technique can provide semi-quantitative, observational constraints of these variables at the scale of the network. For this task, multivariate statistical models are trained on calibration spectra prepared from atmospherically relevant laboratory standards and are applied to ambient samples. Then, the physical basis linking the absorbance profile of this relatively narrow region in the mid-infrared spectrum to the molecular structure is investigated using a classification approach. The multivariate statistical models predict mean carbon number and molecular weight that are consistent with previous values of organic-mass-to-organic-carbon (OM/OC) ratios estimated for the network using different approaches. The results are also consistent with temporal and spatial variations in these quantities associated with aging processes and different source classes (anthropogenic, biogenic, and burning sources). For instance, the statistical models estimate higher mean carbon number for urban samples and smaller, more fragmented molecules for samples in which substantial aging is anticipated.

scholarly journals Examining the Spatio-temporal Dynamics of PM2.5 in Saudi Arabia Using Satellite-derived Data: A Cluster Study

2016 ◽  
Author(s):  
Yusuf Aina ◽  
Elhadi Adam ◽  
Fethi Ahmed
Keyword(s):  
Cluster Analysis ◽  
Saudi Arabia ◽  
Particulate Matter ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
World Health ◽  
Self Organizing Map ◽  
Fine Particulate

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM2.5 values of Saudi cities is performed by using Anselin local Moran&rsquo;s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

scholarly journals Modelling of the public health costs of fine particulate matter and results for Finland in 2015

2019 ◽  
Author(s):  
Jaakko Kukkonen ◽  
Mikko Savolahti ◽  
Yuliia Palamarchuk ◽  
Timo Lanki ◽  
Väinö Nurmi ◽  
...  
Keyword(s):  
Public Health ◽  
Particulate Matter ◽  
Health Outcomes ◽  
Urban Areas ◽  
Transport Model ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Health Costs ◽  
The Public ◽  
Fine Particulate

Abstract. We have developed an integrated tool of assessment that can be used for evaluating the public health costs caused by the concentrations of fine particulate matter (PM2.5) in ambient air. The model can be used in assessing the impacts of various alternative air quality abatement measures, policies and strategies. The model has been applied for the evaluation of the costs of the domestic emissions that influence the concentrations of PM2.5 in Finland in 2015. The model includes the impacts on human health; however, it does not address the impacts on climate change or the state of the environment. First, the national Finnish emissions were evaluated using the Finnish Regional Emission Scenarios model (FRES) on a resolution of 250 × 250 m2 for the whole of Finland. Second, the atmospheric dispersion was analyzed by using the chemical transport model SILAM and the source-receptor matrices contained in the FRES model. Third, the health impacts were assessed by combining the spatially resolved concentration and population datasets, and by analyzing the impacts for various health outcomes. Fourth, the economic impacts for the health outcomes were evaluated. The model can be used to evaluate the costs of the health damages for various emission source categories, for a unit of emissions of PM2.5. It was found that economically the most effective measures would be the reduction of the emissions in urban areas of (i) road transport, (ii) non-road vehicles and machinery, and (iii) residential wood combustion. The reduction of the precursor emissions of PM2.5 was clearly less effective, compared with reducing directly the emissions of PM2.5. We have also designed a user-friendly web-based tool of assessment that is available open access.

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