The Spatial and Temporal Variation of Measured Urban PM10 and PM2.5 in the Helsinki Metropolitan Area

Pohjola, Mia; Kousa, Anu; Kukkonen, Jaakko; Härkönen, Jari; Karppinen, Ari; Aarnio, Päivi; Koskentalo, Tarja

doi:10.1007/978-94-010-0312-4_14

Cited by 25 publications

(23 citation statements)

References 14 publications

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“…If the boundary layer is stable, locally emitted or re-suspended particles stay in a smaller volume (Väkevä et al, 2000). In practice, mixing depend on wind speed, turbulence, and relative humidity (Pohjola et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Urban aerosol number size distributions

et al. 2004

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Abstract. Aerosol number size distributions have been measured since 5 May 1997 in Helsinki, Finland. The presented aerosol data represents size distributions within the particle diameter size range 8-400 nm during the period from May 1997 to March 2003. The daily, monthly and annual patterns of the aerosol particle number concentrations were investigated. The temporal variation of the particle number concentration showed close correlations with traffic activities. The highest total number concentrations were observed during workdays; especially on Fridays, and the lowest concentrations occurred during weekends; especially Sundays. Seasonally, the highest total number concentrations were observed during winter and spring and lower concentrations were observed during June and July. More than 80% of the number size distributions had three modes: nucleation mode (D p <30 nm), Aitken mode (20-100 nm) and accumulation mode (D p >90 nm). Less than 20% of the number size distributions had either two modes or consisted of more than three modes. Two different measurement sites were used; in the first (Siltavuori, 5.5.1997(Siltavuori, 5.5. -5.3.2001, the arithmetic means of the particle number concentrations were 7000 cm −3 , 6500 cm −3 , and 1000 cm −3 respectively for nucleation, Aitken, and accumulation modes. In the second site (Kumpula, 6.3.2001(Kumpula, 6.3. -28.2.2003) they were 5500 cm −3 , 4000 cm −3 , and 1000 cm −3 . The total number concentration in nucleation and Aitken modes were usually significantly higher during workdays than during weekends. The temporal variations in the accumulation mode were less pronounced. The lower concentrations at Kumpula were mainly due to building construction and also the slight overall decreasing trend during these years. During the site changing a period of simultaneous measurements over two weeks were performed showing nice correlation at both sites.

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Section: Introductionmentioning

confidence: 99%

Urban aerosol number size distributions

et al. 2004

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“…As in the case of the UK, the measured fine particulate matter concentrations (PM 2.5 ) only show moderate spatial and temporal variation (e.g., Pohjola et al, 2002;Karppinen et al, 2005). A more substantial variation is found for the urban concentrations of PM 10 (Kukkonen et al, 2001) as a result of local variations in the coarse fraction emissions.…”

Section: Introductionmentioning

confidence: 94%

Evaluation and application of a statistical model for assessment of long-range transported proportion of PM2.5 in the United Kingdom and in Finland

Kukkonen

Sokhi

Luhana

et al. 2008

Atmospheric Environment

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We have developed a simple statistical model for the estimation of the long-range transport (LRT) contribution to PM 2.5 concentrations. The modelling is based on linear regressions of the 'Co-operative programme for monitoring and evaluating the long-range transmission of air pollutants in Europe' (EMEP) background ionic components (SO 4 2À , NO 3 À , NH 4 + ) with the monitored PM 2.5 concentrations. We present an evaluation and application of the model against data measured in the United Kingdom (UK) and in Finland. We have studied the correlation of ion sum values with the PM 2.5 data measured at two EMEP stations in Finland (i.e., a comparison of ionic sum and PM 2.5 at one station). The statistical correlations of the PM 2.5 concentrations with the ion sum values were very high (R 2 varied from 0.77 to 0.83) at both of the stations considered; this provides confidence that the ion sum is a good proxy variable for the LRT PM 2.5 . The comparison of different modelling options using the data measured in the UK showed that the regression model gave systematically substantially better results than the model using merely sulphate concentrations. Similarly, using the distance weighted ion sum based on data from two EMEP stations gave better correlation, compared with the option of using only one EMEP station. The evaluated average LRT contribution accounted for 35-37% of the regional air PM 2.5 concentrations in UK from 1998 to 2000. The corresponding contributions at two urban stations in London were 24-31%. We conclude that the model is a useful and simple tool for the assessment of LRT PM 2.5 that is applicable within a fairly good accuracy. r

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“…In addition to the impact of urbanization and rapid population growth, the pollution levels in the cities are modulated by meteorological factors [6]. Most importantly, the depth of mixing layer (the lower layer of troposphere mixing surface emissions) often depends on solar radiation and thus temperature in the area.…”

Section: Introductionmentioning

confidence: 99%

Modeling PM_2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Deters

Žalakevičiūtė

González

et al. 2017

Journal of Electrical and Computer Engineering

125

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Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM 2.5 ). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM 2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (<10 g/m 3 ) versus high (>25 g/m 3 ) and low (<10 g/m 3 ) versus moderate (10-25 g/m 3 ) concentrations of PM 2.5 . A regression analysis suggests a better prediction of PM 2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM 2.5 concentrations from meteorological data.

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The Spatial and Temporal Variation of Measured Urban PM10 and PM2.5 in the Helsinki Metropolitan Area

Cited by 25 publications

References 14 publications

Urban aerosol number size distributions

Urban aerosol number size distributions

Evaluation and application of a statistical model for assessment of long-range transported proportion of PM2.5 in the United Kingdom and in Finland

Modeling PM_2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

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The Spatial and Temporal Variation of Measured Urban PM10 and PM2.5 in the Helsinki Metropolitan Area

Cited by 25 publications

References 14 publications

Urban aerosol number size distributions

Urban aerosol number size distributions

Evaluation and application of a statistical model for assessment of long-range transported proportion of PM2.5 in the United Kingdom and in Finland

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

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Product

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Modeling PM_2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters