Skip to main content

Advertisement

SpringerLink
Log in

Integrating meteorological factors for better understanding of the urban form-air quality relationship

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

Understanding the urban form-air quality relationship is essential to mitigate intra-urban air pollution and to improve urban ecology. However, few studies considered urban form and meteorological factors integratively and analyzed their synthetic effects on air pollution.

Objectives

We investigate how to model the integrated effects on the spatiotemporal distribution of PM2.5 in the Atlanta metropolitan area to improve the understanding of the urban form-air quality relationship.

Methods

Two groups of models are developed: one uses urban form only and the other uses wind-direct urban form. Relative humidity, wind speed, and temperature are included as control variables. Both linear (Multiple Linear Regression) and nonlinear models (Random Forest and Artificial Neural Network) are constructed and tested with both tenfold cross-validation and field PM2.5 data obtained from a portable device, AirBeam2.

Results

Random Forest overall outperforms other models suggesting that the urban form-air quality relationship is most likely to be nonlinear. Additionally, the group using wind-direct urban form outperforms the other group and the contribution of the same urban form metrics differs in different wind sections proving that meteorological factors and urban form have synthetic effects on PM2.5. Finally, the patch density, dominance, and aggregation of roads and vegetation, demonstrate higher attribute significance than other urban form metrics.

Conclusions

Urban planners, practitioners, and policymakers need to carefully consider not only the spatial configuration of roads and vegetation but also the local climate patterns to minimize intra-urban air pollution effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Arain M, Blair R, Finkelstein N, Brook J, Sahsuvaroglu T, Beckerman B, Zhang L, Jerrett M (2007) The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies. Atmos Environ 41:3453–3464

    CAS  Google Scholar 

  • Avise J, Chen J, Lamb B, Wiedinmyer C, Guenther A, Salathé E, Mass C (2009) Attribution of projected changes in summertime US ozone and PM2. 5 concentrations to global changes. Atmos Chem Phys 9:1111–1124

    CAS  Google Scholar 

  • Barker JR, Tingey DT (2012) Air pollution effects on biodiversity. Springer, Berlin

    Google Scholar 

  • Bechle MJ, Millet DB, Marshall JD (2011) Effects of income and urban form on urban NO2: global evidence from satellites. Environ Sci Technol 45:4914–4919

    CAS  PubMed  Google Scholar 

  • Bereitschaft B, Debbage K (2013) Urban form, air pollution, and CO2 emissions in large US metropolitan areas. Prof Geogr 65:612–635

    Google Scholar 

  • Biau G, Scornet E (2016) A random forest guided tour. Test 25:197–227

    Google Scholar 

  • Borrego C, Martins H, Tchepel O, Salmim L, Monteiro A, Miranda AI (2006) How urban structure can affect city sustainability from an air quality perspective. Environ Model Softw 21:461–467

    Google Scholar 

  • Boznar M, Lesjak M, Mlakar P (1993) A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos Environ Part B Urban Atmos 27:221–230

    Google Scholar 

  • Breiman L (2001) Random forests. Machine Learn 45:5–32

    Google Scholar 

  • Brokamp C, Jandarov R, Rao M, LeMasters G, Ryan P (2017) Exposure assessment models for elemental components of particulate matter in an urban environment: a comparison of regression and random forest approaches. Atmos Environ 151:1–11

    CAS  Google Scholar 

  • Brookings (2003) Atlanta in focus: a profile from Census 2000. Available at www.brookings.edu/research/atlanta-in-focus-a-profile-from-census-2000

  • Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360:1233–1242

    CAS  PubMed  Google Scholar 

  • Bureau USC (2018). Available at www.census.gov

  • Buyantuyev A, Wu J, Gries C (2010) Multiscale analysis of the urbanization pattern of the Phoenix metropolitan landscape of USA: time, space and thematic resolution. Landsc Urban Plan 94:206–217

    Google Scholar 

  • Cho H-S, Choi M (2014) Effects of compact urban development on air pollution: empirical evidence from Korea. Sustainability 6:5968–5982

    CAS  Google Scholar 

  • Contreras L, Ferri C (2016) Wind-sensitive interpolation of urban air pollution forecasts. Procedia Comput Sci 80:313–323

    Google Scholar 

  • Covello VT, Merkhoher MW (2013) Risk assessment methods: approaches for assessing health and environmental risks. Springer, New York

    Google Scholar 

  • De Groot RS, Alkemade R, Braat L, Hein L, Willemen L (2010) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol Complex 7:260–272

    Google Scholar 

  • De Hoogh K, Wang M, Adam M, Badaloni C, Beelen R, Birk M, Cesaroni G, Cirach M, Declercq C, Dedele A (2013) Development of land use regression models for particle composition in twenty study areas in Europe. Environ Sci Technol 47:5778–5786

    PubMed  Google Scholar 

  • Donnelly A, Naughton O, Misstear B, Broderick B (2016) Maximizing the spatial representativeness of NO2 monitoring data using a combination of local wind-based sectoral division and seasonal and diurnal correction factors. J Environ Sci Health Part A 51:1003–1011

    CAS  Google Scholar 

  • Dulal HB, Brodnig G, Onoriose CG (2011) Climate change mitigation in the transport sector through urban planning: a review. Habitat Int 35:494–500

    Google Scholar 

  • Edussuriya PS, Chan A (2015) Analysis of urban morphological attributes and street level air pollution in high-density residential environments in Hong Kong. Living and learning: Research for a better built environment. 49th International Conference of the Architectural Science AssociaTion 2015. The Architectural Science Association and The University of Melbourne, Melbourne

    Google Scholar 

  • Elminir HK (2005) Dependence of urban air pollutants on meteorology. Sci Total Environ 350:225–237

    CAS  PubMed  Google Scholar 

  • Gardner MW, Dorling S (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636

    CAS  Google Scholar 

  • GDOT (2020) Traffic Analysis & Data Application (TADA). Available at https://www.dot.ga.gov

  • Georgescu M (2015) Challenges associated with adaptation to future urban expansion. J Clim 28:2544–2563

    Google Scholar 

  • Georgii H, Busch E, Weber E (1967) Investigation of the temporal and spectral distribution of the emission concentration of carbon monoxide in Frankfurt/Main, Rept. No. 11. Institute for Meteorology and Geophysics, Univ of Frankfurt/Main, Frankfurt

    Google Scholar 

  • Ghassoun Y, Löwner M-O (2017) Land use regression models for total particle number concentrations using 2D, 3D and semantic parameters. Atmos Environ 166:362–373

    CAS  Google Scholar 

  • Guidance E (2001) Improving air quality through land use activities. Transportation and Regional Programs, Division Office of Transportation and Air Quality, US Environmental Protection Agency, Washington, DC

    Google Scholar 

  • Gwinnett Citizen (2018) Atlanta air quality Worsened, Finds 2018 ‘State of the Air’ Report

  • Habermann M, Billger M, Haeger-Eugensson M (2015) Land use regression as method to model air pollution. Previous results for Gothenburg/Sweden. Procedia Eng 115:21–28

    CAS  Google Scholar 

  • HabitatMap (2018). Available at www.habitatmap.org/aircasting

  • Hall MA (1999) Correlation-based feature selection for machine learning

  • Hamilton J (2012) Careers in environmental remediation. Office of Occupational Statistics and Employment Projections, US Bureau, Washington, DC

    Google Scholar 

  • Haykin S (2010) Neural networks and learning machines, 3/E. Pearson Education, Bengaluru

    Google Scholar 

  • He HS, DeZonia BE, Mladenoff DJ (2000) An aggregation index (AI) to quantify spatial patterns of landscapes. Landsc Ecol 15:591–601

    Google Scholar 

  • Heald C, Henze D, Horowitz L, Feddema J, Lamarque JF, Guenther A, Hess P, Vitt F, Seinfeld J, Goldstein A (2008) Predicted change in global secondary organic aerosol concentrations in response to future climate, emissions, and land use change. J Geophys Res Atmos 113

  • Hien P, Loc P, Dao N (2011) Air pollution episodes associated with East Asian winter monsoons. Sci Total Environ 409:5063–5068

    CAS  PubMed  Google Scholar 

  • Hoek G, Beelen R, De Hoogh K, Vienneau D, Gulliver J, Fischer P, Briggs D (2008) A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42:7561–7578

    CAS  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Google Scholar 

  • Irwin EG, Bockstael NE (2007) The evolution of urban sprawl: evidence of spatial heterogeneity and increasing land fragmentation. Proc Natl Acad Sci 104:20672–20677

    CAS  PubMed  Google Scholar 

  • Jacob DJ, Winner DA (2009) Effect of climate change on air quality. Atmos Environ 43:51–63

    CAS  Google Scholar 

  • Jang W, Yao X (2014) Tracking ethnically divided commuting patterns over time: a case study of Atlanta. Prof Geogr 66:274–283

    Google Scholar 

  • Janhäll S (2015) Review on urban vegetation and particle air pollution–deposition and dispersion. Atmos Environ 105:130–137

    Google Scholar 

  • Jiang W, Wang Y, Tsou M-H, Fu X (2015) Using social media to detect outdoor air pollution and monitor air quality index (AQI): a geo-targeted spatiotemporal analysis framework with Sina Weibo (Chinese Twitter). PLoS ONE 10:e0141185

    PubMed  PubMed Central  Google Scholar 

  • Kohonen T (1988) An introduction to neural computing. Neural Netw 1:3–16

    Google Scholar 

  • Larkin A, van Donkelaar A, Geddes JA, Martin RV, Hystad P (2016) Relationships between changes in urban characteristics and air quality in East Asia from 2000 to 2010. Environ Sci Technol 50:9142–9149

    CAS  PubMed  PubMed Central  Google Scholar 

  • Li X, Liu W, Chen Z, Zeng G, Hu C, León T, Liang J, Huang G, Gao Z, Li Z (2015) The application of semicircular-buffer-based land use regression models incorporating wind direction in predicting quarterly NO2 and PM10 concentrations. Atmos Environ 103:18–24

    CAS  Google Scholar 

  • Li Y, Chen Q, Zhao H, Wang L, Tao R (2015b) Variations in PM10, PM2.5 and PM1.0 in an urban area of the Sichuan Basin and their relation to meteorological factors. Atmosphere 6:150–163

    Google Scholar 

  • Lin H, Ma W, Qiu H, Wang X, Trevathan E, Yao Z, Dong G-H, Vaughn MG, Qian Z, Tian L (2017) Using daily excessive concentration hours to explore the short-term mortality effects of ambient PM2. 5 in Hong Kong. Environ Pollut 229:896–901

    CAS  PubMed  Google Scholar 

  • Liu Y, Wu J, Yu D (2017) Characterizing spatiotemporal patterns of air pollution in China: a multiscale landscape approach. Ecol Ind 76:344–356

    CAS  Google Scholar 

  • Liu Y, Wu J, Yu D (2018a) Disentangling the complex effects of socioeconomic, climatic, and urban form factors on air pollution: a case study of China. Sustainability 10:776

    Google Scholar 

  • Liu Y, Wu J, Yu D, Hao R (2018b) Understanding the patterns and drivers of air pollution on multiple time scales: the case of northern China. Environ Manag 61:1048–1061

    Google Scholar 

  • Liu Y, Wu J, Yu D, Ma Q (2018c) The relationship between urban form and air pollution depends on seasonality and city size. Environ Sci Pollut Res. 25(16):15554–15567

    CAS  Google Scholar 

  • Lu C, Liu Y (2016) Effects of China’s urban form on urban air quality. Urban Stud 53:2607–2623

    Google Scholar 

  • Luo J, Du P, Samat A, Xia J, Che M, Xue Z (2017a) Spatiotemporal pattern of PM 2.5 concentrations in mainland China and analysis of its influencing factors using geographically weighted regression. Sci Rep 7:1–14

    Google Scholar 

  • Luo J, Du P, Samat A, Xia J, Che M, Xue Z (2017b) Spatiotemporal pattern of PM 2.5 concentrations in mainland China and analysis of its influencing factors using geographically weighted regression. Sci Rep 7:40607

    CAS  PubMed  PubMed Central  Google Scholar 

  • Marquez LO, Smith NC (1999) A framework for linking urban form and air quality. Environ Model Softw 14:541–548

    Google Scholar 

  • Marshall JD, McKone TE, Deakin E, Nazaroff WW (2005) Inhalation of motor vehicle emissions: effects of urban population and land area. Atmos Environ 39:283–295

    CAS  Google Scholar 

  • Martins H (2012) Urban compaction or dispersion? An air quality modelling study. Atmos Environ 54:60–72

    CAS  Google Scholar 

  • McCarty J, Kaza N (2015) Urban form and air quality in the United States. Landsc Urban Plan 139:168–179

    Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Google Scholar 

  • McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at http://www.umass.edu/landeco/research/fragstats/fragstats.html

  • Meng X, Chen L, Cai J, Zou B, Wu C-F, Fu Q, Zhang Y, Liu Y, Kan H (2015) A land use regression model for estimating the NO2 concentration in Shanghai, China. Enviro Res 137:308–315

    CAS  Google Scholar 

  • Met-Office (2015) Beaufort wind force scale. Available at https://www.metoffice.gov.uk/weather/guides/coast-and-sea/beaufort-scale

  • Mikhailuta SV, Lezhenin AA, Pitt A, Taseiko OV (2017) Urban wind fields: phenomena in transformation. Urban Clim 19:122–140

    Google Scholar 

  • Naughton O, Donnelly A, Nolan P, Pilla F, Misstear B, Broderick B (2018) A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach. Sci Total Environ 630:1324–1334

    CAS  PubMed  Google Scholar 

  • NOAA (2010) 1981–2010 U.S. Climate Normals. National Climatic Data Center

  • NOAA (2017) Global climate report-annual 2017. NCEI, NOAA, Asheville

    Google Scholar 

  • Ou J, Liu X, Li X, Chen Y (2013) Quantifying the relationship between urban forms and carbon emissions using panel data analysis. Landsc Ecol 28:1889–1907

    Google Scholar 

  • Pope CA III, Dockery DW (2006) Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc 56:709–742

    CAS  PubMed  Google Scholar 

  • Pye H, Liao H, Wu S, Mickley LJ, Jacob DJ, Henze DK, Seinfeld J (2009) Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States. J Geophys Res Atmos

  • Qin S, Liu F, Wang J, Sun B (2014) Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmos Environ 98:665–675

    CAS  Google Scholar 

  • Rajan SC (2006) Climate change dilemma: technology, social change or both? An examination of long-term transport policy choices in the United States. Energy Policy 34:664–679

    Google Scholar 

  • Renhe Z, Li Q, Zhang R (2014) Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013. Sci China Earth Sci 57:26–35

    Google Scholar 

  • Rodríguez MC, Dupont-Courtade L, Oueslati W (2016) Air pollution and urban structure linkages: evidence from European cities. Renew Sustain Energy Rev 53:1–9

    Google Scholar 

  • Samuel M (2015) Following The Trail Of Atlanta’s Winter Air Pollution. Wabe. Availale at https://cp.wabe.org/post/following-trail-atlantas-winter-air-pollution

  • Seaman NL (2000) Meteorological modeling for air-quality assessments. Atmos Environ 34:2231–2259

    CAS  Google Scholar 

  • She Q, Peng X, Xu Q, Long L, Wei N, Liu M, Jia W, Zhou T, Han J, Xiang W (2017) Air quality and its response to satellite-derived urban form in the Yangtze River Delta, China. Ecol Ind 75:297–306

    Google Scholar 

  • Shen J, Gao Z, Ding W, Yu Y (2017) An investigation on the effect of street morphology to ambient air quality using six real-world cases. Atmos Environ 164:85–101

    CAS  Google Scholar 

  • Shi Y, Lau KK, Ng E (2016) Developing street-level PM2.5 and PM10 land use regression models in high-density Hong Kong with urban morphological factors. Environ Sci Technol 50:8178–8187

    CAS  PubMed  Google Scholar 

  • Shi Y, Lau KK-L, Ng E (2017) Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment. Environ Res 157:17–29

    CAS  PubMed  Google Scholar 

  • Shi Y, Ren C, Lau KK-L, Ng E (2019) Investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation using mobile monitoring and WUDAPT. Landsc Urban plan 189:15–26

    Google Scholar 

  • Silva LT, Monteiro JP (2016) The Influence of Urban Form on Environmental Quality within a Medium-sized City. Procedia Eng 161:2046–2052

    Google Scholar 

  • Simpson JE (1994) Sea breeze and local winds. Cambridge University Press, Cambridge

    Google Scholar 

  • Stone B Jr (2008) Urban sprawl and air quality in large US cities. J Environ Manag 86:688–698

    CAS  Google Scholar 

  • Suleiman A, Tight M, Quinn A (2016) Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10. Transp Res Part D Transp Environ 49:301–312

    Google Scholar 

  • Tian Y, Yao X, Chen L (2019) Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics. Comput Environ Urban Syst 75:35–48

    Google Scholar 

  • Tsai Y-H (2005) Quantifying urban form: compactness versus’ sprawl’. Urban Stud 42:141–161

    Google Scholar 

  • Vienneau D, De Hoogh K, Briggs D (2009) A GIS-based method for modelling air pollution exposures across Europe. Sci Total Environ 408:255–266

    CAS  PubMed  Google Scholar 

  • Witten IH, Frank E, Hall MA (2016) Data Mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington

    Google Scholar 

  • Wu J, Xie W, Li W, Li J (2015) Effects of urban landscape pattern on PM2.5 pollution—a Beijing case study. PLoS ONE 10:e0142449

    PubMed  PubMed Central  Google Scholar 

  • Wu X, Vu TV, Shi Z, Harrison RM, Liu D, Cen K (2018) Characterization and source apportionment of carbonaceous PM2.5 particles in China—a review. Atmos Environ 189:187–212

    CAS  Google Scholar 

  • Yadan H, Zijiang Z (2009) Climatic characteristics of haze in China. Meteorol Mon 35:72–78

    Google Scholar 

  • Yu R, Yang Y, Yang L, Han G, Move OA (2016) RAQ–a random forest approach for predicting air quality in urban sensing systems. Sensors 16:86

    Google Scholar 

  • Yuan Q, Yang L, Dong C, Yan C, Meng C, Sui X, Wang W (2014) Temporal variations, acidity, and transport patterns of PM 2.5 ionic components at a background site in the Yellow River Delta, China. Air Qual Atmos Health 7:143–153

    CAS  Google Scholar 

  • Zhang C, Ni Z, Ni L (2015a) Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors. Phys A 438:114–123

    Google Scholar 

  • Zhang Y, Kwok KC, Liu X-P, Niu J-L (2015b) Characteristics of air pollutant dispersion around a high-rise building. Environ Pollut 204:280–288

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Georgia, Athens, GA, 30602, USA

    Ye Tian, Xiaobai A. Yao, Lan Mu & Qinjin Fan

  2. Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA

    Yijun Liu

Authors
  1. Ye Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaobai A. Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lan Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qinjin Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yijun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaobai A. Yao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 646 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, Y., Yao, X.A., Mu, L. et al. Integrating meteorological factors for better understanding of the urban form-air quality relationship. Landscape Ecol 35, 2357–2373 (2020). https://doi.org/10.1007/s10980-020-01094-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-020-01094-6

Keywords

Search

Navigation