Integrated decision support system for urban air quality assessment

Lim, Ling L.; Hughes, Susan; Hellawell, E. E.

doi:10.1016/j.envsoft.2004.04.013

Cited by 50 publications

(18 citation statements)

References 2 publications

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“…Ambient air quality standards were established by many countries around the world together with air quality guidelines by World Health Organization (WHO) for the protection of the local, regional and global atmosphere (Lim et al, 2005). Routine air quality monitoring studies are of particular importance in order to identify long-term air quality trends, assess the effectiveness of air quality control regulations and improve air quality management efforts (Özden et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Comparison of NO2 and SO2 Measurements Using Different Passive Samplers in Tropical Environment

Balasubramanian

et al. 2014

Aerosol Air Qual. Res.

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This report describes the results of an inter-comparison study conducted for the sampling and analysis of NO 2 and SO 2 by using different type of passive samplers in tropical urban environment. For this purpose, field experiments were proposed to be conducted by two round robin tests from Sept. 2007 to Aug. 2008 in Singapore. A set of different types of passive samplers have been exposed to ambient air at atmospheric research station in National University of Singapore along with suitable active samplers (as reference method) for a sufficient period of time (3-4 weeks). Annular denuder system (ADS) active sampling data showed that NO 2 concentration level (23.8-28.1 ppb) in Singapore was slightly higher than the WHO guideline but SO 2 concentration (12.5-14.9 ppb) was approximately twice the value of WHO guideline. For the two round robin tests, accuracy analysis by using active sampling data as reference demonstrated that grand mean values from passive air sampling (PAS) and active sampler average concentrations, for both NO 2 and SO 2 , are in close agreement with each other. Precision evaluation based on the triplicate samplers co-located at the same sampling siteSingapore showed that most of the passive samplers applied here had repeatability. ANOVA statistical analysis also showed no significant difference between the measurement data obtained by different type of passive samplers for both round robin tests in Singapore.

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Section: Introductionmentioning

confidence: 99%

Comparison of NO2 and SO2 Measurements Using Different Passive Samplers in Tropical Environment

Balasubramanian

et al. 2014

Aerosol Air Qual. Res.

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“…The results, including the earlier uncertainty estimates and model validation Nahorski 2015, Holnicki et al 2016), may be useful in formulation of the respective regulatory actions and emission reduction strategy (compare e.g. Carnevale et al 2012, Lim et al 2005, Mediavilla-Sahagún and ApSimon 2006, Pisoni et al 2010.…”

Section: Warsaw Case Study Implementationmentioning

confidence: 99%

“…Warsaw, similarly as many other European agglomerations (Lim et al 2005, Calori et al 2006, Mediavilla-Sahagún and ApSimon 2006, Buchholz et al 2013, has recently suffered from high concentrations of some air pollutants which characterize the urban atmospheric environment. These are usually particulate matter, sulfur-and nitrogen oxides, carbon monoxide, some heavy metals, as well as polycyclic aromatic hydrocarbons in some cases.…”

Section: Introductionmentioning

confidence: 99%

Air quality modeling for Warsaw agglomeration

Holnicki¹,

Kałuszko²,

Nahorski³

et al. 2017

Archives of Environmental Protection

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Abstract:The paper investigates the air quality in the urban area of Warsaw, Poland. Calculations are carried out using the emissions and meteorological data from the year 2012. The modeling tool is the regional CALMET/CALPUFF system, which is used to link the emission sources with the distributions of the annual mean concentrations. Several types of polluting species that characterize the urban atmospheric environment, like PM 10 , PM 2.5 , NOx, SO 2 , Pb, B(a)P, are included in the analysis. The goal of the analysis is to identify the most polluted districts and polluting compounds there, to check where the concentration limits of particular pollutants are exceeded. Then, emission sources (or emission categories) which are mainly responsible for violation of air quality standards and increase the adverse health effects, are identifi ed. The modeling results show how the major emission sources -the energy sector, industry, traffi c and the municipal sector -relate to the concentrations calculated in receptor points, including the contribution of the transboundary infl ow. The results allow to identify districts where the concentration limits are exceeded and action plans are needed. A quantitative source apportionment shows the emission sources which are mainly responsible for the violation of air quality standards. It is shown that the road transport and the municipal sector are the emission classes which substantially affect air quality in Warsaw. Also transboundary infl ow contributes highly to concentrations of some pollutants. The results presented can be of use in analyzing emission reduction policies for the city, as a part of an integrated modeling system. Unauthenticated Download Date | 5/9/18 7:05 PM

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“…The problem related to air pollution in urbanized areas is ranked high on the list of priority environmental concerns [4,6,14]. Urban-scale pollution estimations are a sophisticated modeling issue for computational reasons because of complexity of emission field, the complicated building orography, and wind-field effects.…”

Section: Introduction: Air Pollution Transport Modelingmentioning

confidence: 99%

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

Holnicki

Nahorski

2015

Environ Model Assess

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Air pollution models are often used to support decisions in air quality management. Due to the complexity of the forecasting system and difficulty in acquiring precise enough input data, an environmental prognosis of air quality with an analytical model of the air pollution dispersion is burdened with a substantial share of uncertainty, especially as regards urban areas. To ignore the uncertainty in the modeling would lead to incorrect policy decisions, with further negative environmental and health consequences. This paper presents a case study which shows how emission uncertainty of air pollutants generated by the industry, traffic, and the municipal sector relates to concentrations measured at receptor points. The computational experiment was implemented in the Warsaw metropolitan area, Poland. The main source of this adverse environmental impact is the transportation system, including the transit traffic. The Monte Carlo technique was used for assessing the key uncertainty factors. Several types of pollution species that are characteristic for the urban atmospheric environment (e.g., PM 10 , PM 2.5 , NO x , SO 2 , Pb) were included in the analysis. The results show significant spatial variability of the modeled uncertainty. The reason of this variability is discussed in detail. It depends not only on the category of the emission source but also on the contributing emission sources and their quantity.

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Integrated decision support system for urban air quality assessment

Cited by 50 publications

References 2 publications

Comparison of NO2 and SO2 Measurements Using Different Passive Samplers in Tropical Environment

Comparison of NO2 and SO2 Measurements Using Different Passive Samplers in Tropical Environment

Air quality modeling for Warsaw agglomeration

Emission Data Uncertainty in Urban Air Quality Modeling—Case Study

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