- Email: [email protected]
Integrated air quality monitoring to identify local environmental impacts and mitigation from freight transport
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Transportation Research Procedia 00 (2018) 000–000 Available online at www.sciencedirect.com Transportation Research Procedia 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 39 (2019) 4–13 www.elsevier.com/locate/procedia
Green Cities 2018 Green Cities 2018
Integrated air quality monitoring to identify local environmental Integrated air quality monitoring to identify local environmental impacts and mitigation from freight transport impacts and mitigation from freight transport a, * b a Richard Baldaufa, *, Parik Deshmukhb, Vlad Isakova Richard Baldauf , Parik Deshmukh , Vlad Isakov 0F
a a
0F
U.S. Environmental Protection Agency, Office of Research & Development, Research Triangle Park, North Carolina, USA b JacobsAgency, Technology Research&Triangle Park, North Carolina, USA U.S. Environmental Protection OfficeInc., of Research Development, Research Triangle Park, North Carolina, USA b Jacobs Technology Inc., Research Triangle Park, North Carolina, USA
Abstract Abstract Freight transport has been shown to impact air quality at the local and regional scale. Health studies indicate that exposure to Freightexhaust transport hasresult been in shown to impact qualityInat addition, the local other and regional studies indicate that exposure to diesel can adverse health air effects. studies scale. indicateHealth that populations spending significant diesel exhaust result adverse health effects. freight In addition, other indicate that spending significant amounts of timecan near busyinroads, often supporting transport, alsostudies face increased riskspopulations for a number of adverse health effects including asthma, disease, birth and transport, developmental issues and even premature mortality. As a result, amounts of time near busycardiovascular roads, often supporting freight also face increased risks for a number of adverse health effects including cardiovascular disease, birth and activity developmental and even mortality. As a result, characterizing the asthma, impact of freight transport and vehicular on localissues and regional air premature quality must be comprehensive in characterizing the impact of freight transport and vehicular on monitoring local and regional qualityfixed-site must be measurements comprehensiveand in order to understand and mitigate these health risks. Ambient activity air quality methodsair include mobiletomonitoring instruments that range expensive, research-grade to low-cost, portable sensors. This order understand using and mitigate these health risks.from Ambient air quality monitoringanalyzers methods include fixed-site measurements and mobile monitoring using instruments rangethat from expensive, analyzers to low-cost, portable This paper will present the results from casethat studies have integratedresearch-grade fixed and mobile monitoring techniques using asensors. full range of paper will present theinresults case studies that have integrated fixed and mobile monitoring techniques using full range of air quality analyzers order from to take advantage of the benefits of each technique. The case studies encompass theaevaluation busy in roads, be implemented for impacts rail and port withencompass significantthe freight activity. air quality near analyzers orderbut to can takealso advantage of the benefits of eachnear technique. The activities case studies evaluation of air quality busy also roads,highlight but can also be implemented for impacts near railhow and port withbuilt significant freight activity. These casenear studies the use of these methods to evaluate urbanactivities green and infrastructure can be These case studies also local highlight the use these methods to evaluate how urban green and built infrastructure can be implemented to improve air quality nearofthese sources implemented to improve local air quality near these sources © 2018 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. © 2018 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Green Logistics Logistics for for Greener Greener Cities Cities 2018. 2018. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. Keywords: Air Quality; Mobile Monitoring; Traffic; Freight; Green Infrastructure Keywords: Air Quality; Mobile Monitoring; Traffic; Freight; Green Infrastructure
*Corresponding author. Tel.: +1-919-541-4386. E-mail address:author. [email protected] *Corresponding Tel.: +1-919-541-4386. E-mail address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2352-1465 © 2018 Thearticle Authors. Published by Elsevier B.V. Selection under responsibility of the scientific of Green Logistics for Greener Cities 2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 2352-1465 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 10.1016/j.trpro.2019.06.002
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Richard Baldauf et al. / Transportation Research Procedia 39 (2019) 4–13 Richard Baldauf, Parik Deschmukh, Vlad Isakov/ Transportation Research Procedia 00 (2018) 000–000
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1. Introduction Freight transport can have significant impacts on air quality in the communities through which goods movement occurs. These impacts can result from individual or a combination of roadway, rail, ship and air sources. Numerous studies have shown that exposures to air pollution from these transportation sources can lead to increased adverse health effect risks, especially for populations living, working and going to school near these sources. The health effects include respiratory, cardiovascular, developmental, neurological, cancer and premature mortality (see Health Effects Institute, 2010 for meta-analysis on studies and causal, suggestive and insufficient data determinations and Ghosh et al., 2013 for childhood leukemia meta-analysis). Understanding the extent of air pollution impacts on local communities from freight transport provides important information to design effective and efficient mitigation strategies. These pollution control strategies can include regulating emissions from the sources, restricting or rerouting vehicle activity, and using urban and community design to reduce the local populations exposure to air pollutants. The United States Environmental Protection Agency (EPA) has investigated community-scale impacts from air pollution emissions by freight transport and other vehicular activity to evaluate the effectiveness of current pollution control strategies as well as develop new techniques for reducing air pollution exposures. EPA has conducted a number of studies integrating multiple air pollution measurement techniques in order to effectively evaluate pollution mitigation options applicable to freight transport and other transportation-related impacts. The use of multiple measurement techniques provides the ability to utilize the strengths of each method while offsetting the limitations inherent in each sampling type. The following paper describes the methods used by EPA to characterize community-scale air quality near freight movement and other transportation facilities. The paper also provides case studies on the use of these methods to evaluate urban design techniques to improve local air quality, in particular how roadside vegetation and urban green infrastructure can improve air quality. These mitigation options can be applicable to goods movement corridors and facilities to reduce local population exposures, although these mitigation techniques are also relevant for broader transportation and other air pollution source emissions to improve local air quality. The methods used in these case studies can be implemented at the local or regional scale, and supervised by governmental agencies, academics, or non-governmental organizations. 2. Measurement Methods Integrating multiple field and laboratory measurement methods provides a comprehensive picture of the impacts of transportation sources on local air quality as well as the effectiveness of mitigation techniques. The types of techniques include reference method analyzers, low-cost sensors, and mobile monitors. Descriptions of the strengths and limitations of these techniques are included in the following section, while Table 1 contains a list of commonly used analyzers for assessing transportation source impacts. 2.1
Reference and Research-Grade Sampling
EPA requires high-quality, accurate and reliable measurement techniques for monitoring to compare with the United States’ National Ambient Air Quality Standards (NAAQS). These samplers have undergone extensive testing and evaluation, with specified quality assurance and control requirements, in order to obtain precise and accurate data. In 2015, EPA established the National Near-Road monitoring network, which requires nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter less than 2.5 microns in diameter (PM2.5) reference monitoring near the nations’ largest highways, many of which include extensive truck traffic supporting freight transport (U.S. EPA, 2012). While reference monitoring provides precise and accurate data, the cost to install and maintain this equipment can be relatively expensive. For example, one Near-Road monitoring station with NO2, CO and PM2.5 reference monitors can cost over US$250,000. In addition, these stations require regular maintenance and calibration, typically on a monthly or more frequent basis.
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2.2
3
Low-Cost Sensors
Recent years have seen a rapid growth in the development and use of low-cost, air quality sensors for measuring ambient air quality as well as personal exposures to air pollutants (EPA, 2017). These sensors are typically battery operated, so can be used in remote locations and areas with no external power sources. In addition, these sensors are small and lightweight; thus, the samplers can be fitted on a person to measure pollution during daily or scripted activities, placed on cars and bicycles to measure air quality while driven, or placed in unique locations where reference monitors cannot go, such as hung on light posts or window sills. These samplers can range in cost from US$100 to several thousand US dollars, often enabling a large number of samplers to be located in an area of interest to allow “saturation” sampling (Baldauf, 2002). The key limitation of low-cost sensors is the reduced accurancy and precision compared with reference method samplers (U.S. EPA, 2017). Careful quality assurance procedures must be implemented in order to quantify the uncertainty in air quality measurements. This uncertainty in measurements often requires a large number of samplers as well as extensive collocated measurements to quantify the confidence in the measurements. In addition, many of these samplers are prone to breaking or sensitive to weather conditions and require proper housing of the sampling inlet and system. 2.3
Mobile Monitoring
Mobile monitoring consists of samplers being regularly moved in order to collect air quality data at many locations to “map” air quality in an area of interest (Hagler et al., 2010). Mobile monitoring has used on-road cars, bicycles, carts, and even people walking with samplers as a means of moving the analyzers and collecting data at various locations. The analyzers used in mobile monitoring can include reference and other research-grade equipment, low-cost sensors and a combination of these methods. The use of mobile monitoring can provide a greatly enhanced understanding of how air pollutant concentrations vary at different locations. For example, mobile monitoring can be used to identify pollutant gradients away from sources like roadways and rail yards. While mobile monitoring can improve the mapping of pollutant concentrations spatially, this technique does not provide insights on how concentrations may vary over time. Since the mobile monitoring platforms move within the area, this technique cannot measure at a location over an extended time yet still obtain the spatially-resolved data. Table 1. Typical measurement methods used for assessing the impacts of freight and other transportation source emissions, including reference methods and low-cost sensors.
Measurement Parameter
Sampling Approach
Instrument Make/Model
Carbon Monoxide (CO)
nondispersive infrared
Model 48i-TLE Thermo Scientific
Oxides of nitrogen (NO/NO2/NOx)
chemiluminescence
Model 200A API
Carbon Monoxide (CO)
Quantum Cascade Laser
Aerodyne Research, Inc.
Nitrogen Dioxide (NO2)
Cavity Attenuation Phase Shift
Aerodyne Research, Inc.
Particle number concentration (size range 5.6-560 nm, 32 channels)
Engine Exhaust Particle Sizer
TSI, Inc., Model 3090
Sample Type and Frequency Fixed-Site Reference Continuous 1-minute Fixed-Site Reference Continuous 1-minute Fixed or Mobile Research 1-second Fixed or Mobile Research 1-second Fixed or Mobile Research 1-second
Richard Baldauf, Parik Deschmukh, Vlad Isakov/ Transportation Research Procedia 00 (2018) 000–000 Richard Baldauf et al. / Transportation Research Procedia 39 (2019) 4–13
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Measurement Parameter
Sampling Approach
Instrument Make/Model
Black Carbon (BC)
Single-channel Aethalometer
Magee Scientific, AE-51
Nitrogen Dioxide (NO2)
Electrode Sensor
CairPol CairClip
Longitude and Latitude
Global positioning system
Crescent R100, Hemisphere GPS
Video
Webcam
Wind speed and direction
sonic anemometer
RM Young 81000
7
Sample Type and Frequency Fixed or Mobile Sensor 1-second/ 1-minute Fixed or Mobile Sensor 1-second/ 1-minute Fixed or Mobile Research 1-second Fixed or Mobile Research 1-second Fixed-Site Research 1-second
3. Case Studies: Urban Built and Green Infrastructure Impacts on Air Quality Experiments have been conducted in the United States to investigate how roadside features, particularly solid noise barriers and roadside vegetation, can be used to improve local air quality and reduce air pollution exposures for the nearby population. An understanding of the impacts of these urban features required the use of reference and research-grade monitors, low-cost sensors and mobile monitoring. For a study in Raleigh, North Carolina, USA, a combination of mobile monitoring was integrated with fixed reference and low-cost sensor measurements to understand how a solid noise barrier with and without vegetation affected near-road pollutant concentrations compared to a similar location with no obstructions to air flow (Baldauf et al., 2008; Cho et al., 2009). Figure 1 shows an overview of the sampling site with reference method samplers housed in the trailer, as well as fixed, low-cost sensors located at increasing distances away from the road. Figure 2 shows temporal measurements from the reference and low-cost sensor fixed sites, highlighting the variability of air pollution during the day. In particular, the figure shows that pollutant concentrations of many pollutants were elevated during the morning when traffic volumes were high and wind speeds were low and generally from the road due to morning inversions.
Figure 1. Integrated monitoring at the near-road clearing site. Reference method and meteorological samplers were located in the shelter to the right. Low-cost, portable samplers were located on the poles in the model to form a
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transect away from the road. Mobile monitoring occurred with the vehicle to the left, shown parked in this picture but also collected samples while driving.
Figure 2. Real-time a) wind speed and direction, b) nitrogen oxide (NO), c) carbon monoxide (CO), d) ammonia, e) benzene and f) napthalene measured with reference and research-grade samplers at the near- road site shown in Figure 1 (Baldauf et al., 2008a). To assess the impact from the solid noise barrier with and without vegetation, mobile monitoring was required since the area of interest was a residential neighbourhood where large, reference samplers could not remain during the day and low-coast sensors could not be observed or accessed due to security concerns. However, the reference method and low cost fixed sampler data was used to determine that morning hours were the best to conduct the mobile monitoring due to the high impacts. In addition, the mobile monitors were parked next to the fixed monitors at the beginning and end of each sampling day for quality assurance analyses. Figure 3 shows the results of the mobile monitoring. The results indicated that the solid noise barrier reduced near-road pollution concentrations by 15-20 percent. When vegetation was directly behind the solid
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barrier, the pollutant levels were reduced even more, as much as 50 percent lower than a similar distance from the highway with no obstruction to air flow.
Figure 3. Mobile monitoring data collected along a sampling route that encompassed the clearing site shown in Figure 1, a behind noise barrier only site, and a behind barrier with vegetation site. The results highlight the reduction in downwind air pollution behind the noise barrier, with a further decrease behind the barrier with vegetation (Baldauf et al., 2008b).
A similar study was conducted in Phoenix, Arizona, USA to evaluate how solid noise barriers impact nearby, urban air quality. This study, also conducted in a residential area, utilized low-cost sensors and mobile monitoring since reference method samplers could not be located in the study area due to site space restrictions and lack of external power. Given these restrictions, fixed, low cost sensors were placed behind the noise barrier and in a clearing location to determine how pollutant concentrations varied over time. Figure 4 shows a monitoring tower placed behind the barrier and in the clearing, collecting air quality and wind speed/direction data at multiple heights. Mobile monitoring along residential roads traversing the clearing and behind barrier areas provided data on how
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pollutant concentrations varied by distance both behind the barrier and in a clearing with no obstructions to air flow. Figure 5 shows that concentrations were lower behind the barrier as far as 300 meters from the highway, with reductions as high as 50 percent within the first 50 meters of the barrier. While reference sampling was not conducted on-site, EPA did maintain a reference method, NAAQS air quality station approximately 5 km from the study area. Both the mobile and fixed sensors were collocated with the NAAQS station before, during and after the field study for quality assurance assessments.
Figure 4. Aerial view of the Phoenix Noise Barrier Study site with fixed, low-cost sensor locations shown by pins and the mobile monitoring route shown in blue (clearing area) and yellow (behind barrier area). A picture of the noise barrier is shown to the right.
Figure 5 – Left, Nitrogen dioxide (NO2) and right, ultrafine particle (UFP) measurements on the highway and at varying distances from the road in the clearing and behind the solid noise barrier. The distributions show the mean (dot), median, 75/25th percentiles and 95/5th percentiles. All differences are significant with the exception of the on-highway distributions (Baldauf et al., 2016).
A recent study in the San Francisco, California area investigated how roadside vegetation alone impacts nearby urban air quality. As with the study in Phoenix, lack of power and sufficient space prohibited the use of reference method sampling at the study location. As a result, a combination of mobile and fixed, low cost sensors measured air quality in the area, comparing downwind pollutant concentrations in a clearing with no obstructions to air flow with measurements behind roadside vegetation with varying characteristics. Five sites with differing vegetation type, height, thickness and porosity were chosen to identify how these parameters affect downwind pollutant concentrations. Figure 6 shows a picture of the monitoring location and fixed, sensor towers measuring air quality and meteorological parameters. In addition, mobile measurements were collected along the access road parallel to
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the highway with and without roadside vegetation to identify spatial changes in air quality. In addition to continuous driving, the mobile monitoring platform stopped at each site for approximately 10-minutes each day to collect more time resolved data. A second mobile monitoring vehicle remained parked at the clearing site for comparisons. As shown in Figure 7, different vegetation characteristics resulted in varying air pollution levels behind the vegetation. For dense, tall and thick vegetation, downwind air pollution concentrations reduced by more than 50 percent; however, highly porous vegetation with gaps resulted in negligible reductions for particulate pollutants and actually increased concentrations of gaseous pollutants. These results highlight characteristics of roadside vegetation needed for downwind pollutant reductions as seen in other studies as well (see reviews by Ajileth et al, 2017; Baldauf, 2017).
Figure 6. Locations of the vegetation barrier study with the clearing (1), behind bushes approximately 3m tall (2), behind porous bushes with gaps (3), behind thick bushes 4.5m tall (4), behind thick 4.5m bushes with 1m wide gap (5) and behind thick bushes 4.5m tall and trees 10m tall (6). Mobile monitoring vehicles are shown parked at sites (1) and (5) while the low-cost sensor and meteorological measurements are shown at site (3).
Figure 7. Distributions of nitrogen dioxide (NO2) and ultrafine particles (UFP) at each measurement site with the low-cost and mobile monitoring measurements. The distributions show the mean (dot), median, 75/25th percentiles and 95/5th percentiles. All differences are
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significant
4. Conclusions Ambient air quality monitoring techniques each provide unique strengths and limitations. Reference and research-grade instruments collect highly accurate and precise data, but require external power, temperaturecontrolled shelters and can be very costly. Low-cost sensors do not require external power and are inexpensive; however, these instruments are less accurate and reliable. Both sampling systems can provide important information on temporal changes in pollutant concentrations when operated at fixed locations. Mobile monitoring provides expanded spatial measurement capabilities by installing instruments in a moving platform such as a vehicle or on a person walking or biking. Each of these monitoring techniques was integrated for studies conducted in the United States evaluating how urban built and green infrastructure affects local, urban air quality. The results of these studies highlighted how solid noise barriers and roadside vegetation, individually or in combination, can improve local air quality when containing the proper characteristics. The studies also showed that some vegetation types and characteristics can degrade local air quality. Integrating the multiple measurement techniques provides an important mechanism to properly assessing local and urban air quality. These techniques can also be important in the design and evaluation of land use planning and urban infrastructure options for improving air quality. These measurement techniques also provide the data needed to develop and evaluate air quality dispersion models to estimate population exposures to pollution emissions and quantify the benefits of air pollution mitigation strategies including built and green infrastructure (e.g. Baldauf et al., 2017; Gallagher et al., 2015). Acknowledgements The author gratefully acknowledges the EPA and external team members who contributed to the field studies and results described in the case studies in Section 3 including all co-authors and acknowledged personnel in the references cited for each study. Disclaimer The views expressed in this publication are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.There is also the option to include a subheading within the Appendix if you wish. References Baldauf, R.W, (2017) Roadside vegetation design characteristics that can improve local, near-road air quality. Transportation Research Part D: Transport and Environment, 52, pp.354-361. Baldauf, R.W., V. Isakov, A. Venkatram, P. Deshmukh, B. Yang, K.M Zhang (2016) Influence of solid noise barriers on near-road and on-road air quality, Atmospheric Environment, Vol. 129, Pages 265–276. Baldauf, R.W., A. Khlystov, V. Isakov, E. Thoma, G.E. Bowker, T. Long, R. Snow (2008a) Impacts of Noise Barriers on Near-Road Air Quality, Atmospheric Environment. 42: 7502–7507. Baldauf, R.W., E. Thoma, M. Hays, R. Shores, J. Kinsey, B. Gullett, S. Kimbrough, V. Isakov, T. Long, R. Snow, A. Khlystov, J. Weinstein, F. Chen, R. Seila, D. Olson, M.I. Gilmour, S.H. Cho, N. Watkins, P. Rowley, J. Bang (2008b) Traffic and Meteorological Impacts on Near Road Air Quality: Summary of Methods and Trends from the Raleigh Near Road Study, J. Air & Waste Manage Assoc. 58:865–878 Baldauf, R.W., R.W. Wiener, D. Hiest, 2002, Methodology for Siting Ambient Air Monitors at the Neighborhood Scale, J. Air & Waste Manage Assoc, 52:1433-1442. Cho, S.H., H. Tong, J. McGee, R.W. Baldauf, T. Krantz, M.I. Gilmour (2009) Comparative Toxicity of Size-Fractionated Airborne Particulate Matter Collected at Different Distances from an Urban Highway. Environmental Health Perspectives, Vol, 117 (11). Gallagher, J., R.W. Baldauf, C.H. Fuller, P. Kumar, L.W. Gill, A. McNabola (2015) Passive methods for improving air quality in the built environment: A review of porous and solid barriers. Atmos. Environ. 120, pp. 61-70.
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Ghosh, J.K.C., J.E. Heck, M. Cockburn, J. Su, M. Jerrett, B. Ritz, (2013) Prenatal exposure to traffic-related air pollution and risk of early childhood cancers. American journal of epidemiology, 178(8), pp.1233-1239. Hagler, G.S.W., E.D. Thoma, R.W. Baldauf (2010) High-Resolution Mobile Monitoring of Carbon Monoxide and Ultrafine Particle Concentrations in a Near-Road Environment, J. Air & Waste Manage Assoc. 60: 328–336. U.S. Environmental Protection Agency (2017) Air Sensors Toolbox, https://www.epa.gov/air-sensor-toolbox U.S. Environmental Protection Agency (2012) Near-Road NO2 Monitoring Technical Assistance Document, EPA454/B-12-002, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
ScienceDirect ScienceDirect
Transportation Research Procedia 00 (2018) 000–000 Available online at www.sciencedirect.com Transportation Research Procedia 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 39 (2019) 4–13 www.elsevier.com/locate/procedia
Green Cities 2018 Green Cities 2018
Integrated air quality monitoring to identify local environmental Integrated air quality monitoring to identify local environmental impacts and mitigation from freight transport impacts and mitigation from freight transport a, * b a Richard Baldaufa, *, Parik Deshmukhb, Vlad Isakova Richard Baldauf , Parik Deshmukh , Vlad Isakov 0F
a a
0F
U.S. Environmental Protection Agency, Office of Research & Development, Research Triangle Park, North Carolina, USA b JacobsAgency, Technology Research&Triangle Park, North Carolina, USA U.S. Environmental Protection OfficeInc., of Research Development, Research Triangle Park, North Carolina, USA b Jacobs Technology Inc., Research Triangle Park, North Carolina, USA
Abstract Abstract Freight transport has been shown to impact air quality at the local and regional scale. Health studies indicate that exposure to Freightexhaust transport hasresult been in shown to impact qualityInat addition, the local other and regional studies indicate that exposure to diesel can adverse health air effects. studies scale. indicateHealth that populations spending significant diesel exhaust result adverse health effects. freight In addition, other indicate that spending significant amounts of timecan near busyinroads, often supporting transport, alsostudies face increased riskspopulations for a number of adverse health effects including asthma, disease, birth and transport, developmental issues and even premature mortality. As a result, amounts of time near busycardiovascular roads, often supporting freight also face increased risks for a number of adverse health effects including cardiovascular disease, birth and activity developmental and even mortality. As a result, characterizing the asthma, impact of freight transport and vehicular on localissues and regional air premature quality must be comprehensive in characterizing the impact of freight transport and vehicular on monitoring local and regional qualityfixed-site must be measurements comprehensiveand in order to understand and mitigate these health risks. Ambient activity air quality methodsair include mobiletomonitoring instruments that range expensive, research-grade to low-cost, portable sensors. This order understand using and mitigate these health risks.from Ambient air quality monitoringanalyzers methods include fixed-site measurements and mobile monitoring using instruments rangethat from expensive, analyzers to low-cost, portable This paper will present the results from casethat studies have integratedresearch-grade fixed and mobile monitoring techniques using asensors. full range of paper will present theinresults case studies that have integrated fixed and mobile monitoring techniques using full range of air quality analyzers order from to take advantage of the benefits of each technique. The case studies encompass theaevaluation busy in roads, be implemented for impacts rail and port withencompass significantthe freight activity. air quality near analyzers orderbut to can takealso advantage of the benefits of eachnear technique. The activities case studies evaluation of air quality busy also roads,highlight but can also be implemented for impacts near railhow and port withbuilt significant freight activity. These casenear studies the use of these methods to evaluate urbanactivities green and infrastructure can be These case studies also local highlight the use these methods to evaluate how urban green and built infrastructure can be implemented to improve air quality nearofthese sources implemented to improve local air quality near these sources © 2018 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. © 2018 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Green Logistics Logistics for for Greener Greener Cities Cities 2018. 2018. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. Keywords: Air Quality; Mobile Monitoring; Traffic; Freight; Green Infrastructure Keywords: Air Quality; Mobile Monitoring; Traffic; Freight; Green Infrastructure
*Corresponding author. Tel.: +1-919-541-4386. E-mail address:author. [email protected] *Corresponding Tel.: +1-919-541-4386. E-mail address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2352-1465 © 2018 Thearticle Authors. Published by Elsevier B.V. Selection under responsibility of the scientific of Green Logistics for Greener Cities 2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 2352-1465 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 10.1016/j.trpro.2019.06.002
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Richard Baldauf et al. / Transportation Research Procedia 39 (2019) 4–13 Richard Baldauf, Parik Deschmukh, Vlad Isakov/ Transportation Research Procedia 00 (2018) 000–000
5
1. Introduction Freight transport can have significant impacts on air quality in the communities through which goods movement occurs. These impacts can result from individual or a combination of roadway, rail, ship and air sources. Numerous studies have shown that exposures to air pollution from these transportation sources can lead to increased adverse health effect risks, especially for populations living, working and going to school near these sources. The health effects include respiratory, cardiovascular, developmental, neurological, cancer and premature mortality (see Health Effects Institute, 2010 for meta-analysis on studies and causal, suggestive and insufficient data determinations and Ghosh et al., 2013 for childhood leukemia meta-analysis). Understanding the extent of air pollution impacts on local communities from freight transport provides important information to design effective and efficient mitigation strategies. These pollution control strategies can include regulating emissions from the sources, restricting or rerouting vehicle activity, and using urban and community design to reduce the local populations exposure to air pollutants. The United States Environmental Protection Agency (EPA) has investigated community-scale impacts from air pollution emissions by freight transport and other vehicular activity to evaluate the effectiveness of current pollution control strategies as well as develop new techniques for reducing air pollution exposures. EPA has conducted a number of studies integrating multiple air pollution measurement techniques in order to effectively evaluate pollution mitigation options applicable to freight transport and other transportation-related impacts. The use of multiple measurement techniques provides the ability to utilize the strengths of each method while offsetting the limitations inherent in each sampling type. The following paper describes the methods used by EPA to characterize community-scale air quality near freight movement and other transportation facilities. The paper also provides case studies on the use of these methods to evaluate urban design techniques to improve local air quality, in particular how roadside vegetation and urban green infrastructure can improve air quality. These mitigation options can be applicable to goods movement corridors and facilities to reduce local population exposures, although these mitigation techniques are also relevant for broader transportation and other air pollution source emissions to improve local air quality. The methods used in these case studies can be implemented at the local or regional scale, and supervised by governmental agencies, academics, or non-governmental organizations. 2. Measurement Methods Integrating multiple field and laboratory measurement methods provides a comprehensive picture of the impacts of transportation sources on local air quality as well as the effectiveness of mitigation techniques. The types of techniques include reference method analyzers, low-cost sensors, and mobile monitors. Descriptions of the strengths and limitations of these techniques are included in the following section, while Table 1 contains a list of commonly used analyzers for assessing transportation source impacts. 2.1
Reference and Research-Grade Sampling
EPA requires high-quality, accurate and reliable measurement techniques for monitoring to compare with the United States’ National Ambient Air Quality Standards (NAAQS). These samplers have undergone extensive testing and evaluation, with specified quality assurance and control requirements, in order to obtain precise and accurate data. In 2015, EPA established the National Near-Road monitoring network, which requires nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter less than 2.5 microns in diameter (PM2.5) reference monitoring near the nations’ largest highways, many of which include extensive truck traffic supporting freight transport (U.S. EPA, 2012). While reference monitoring provides precise and accurate data, the cost to install and maintain this equipment can be relatively expensive. For example, one Near-Road monitoring station with NO2, CO and PM2.5 reference monitors can cost over US$250,000. In addition, these stations require regular maintenance and calibration, typically on a monthly or more frequent basis.
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2.2
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Low-Cost Sensors
Recent years have seen a rapid growth in the development and use of low-cost, air quality sensors for measuring ambient air quality as well as personal exposures to air pollutants (EPA, 2017). These sensors are typically battery operated, so can be used in remote locations and areas with no external power sources. In addition, these sensors are small and lightweight; thus, the samplers can be fitted on a person to measure pollution during daily or scripted activities, placed on cars and bicycles to measure air quality while driven, or placed in unique locations where reference monitors cannot go, such as hung on light posts or window sills. These samplers can range in cost from US$100 to several thousand US dollars, often enabling a large number of samplers to be located in an area of interest to allow “saturation” sampling (Baldauf, 2002). The key limitation of low-cost sensors is the reduced accurancy and precision compared with reference method samplers (U.S. EPA, 2017). Careful quality assurance procedures must be implemented in order to quantify the uncertainty in air quality measurements. This uncertainty in measurements often requires a large number of samplers as well as extensive collocated measurements to quantify the confidence in the measurements. In addition, many of these samplers are prone to breaking or sensitive to weather conditions and require proper housing of the sampling inlet and system. 2.3
Mobile Monitoring
Mobile monitoring consists of samplers being regularly moved in order to collect air quality data at many locations to “map” air quality in an area of interest (Hagler et al., 2010). Mobile monitoring has used on-road cars, bicycles, carts, and even people walking with samplers as a means of moving the analyzers and collecting data at various locations. The analyzers used in mobile monitoring can include reference and other research-grade equipment, low-cost sensors and a combination of these methods. The use of mobile monitoring can provide a greatly enhanced understanding of how air pollutant concentrations vary at different locations. For example, mobile monitoring can be used to identify pollutant gradients away from sources like roadways and rail yards. While mobile monitoring can improve the mapping of pollutant concentrations spatially, this technique does not provide insights on how concentrations may vary over time. Since the mobile monitoring platforms move within the area, this technique cannot measure at a location over an extended time yet still obtain the spatially-resolved data. Table 1. Typical measurement methods used for assessing the impacts of freight and other transportation source emissions, including reference methods and low-cost sensors.
Measurement Parameter
Sampling Approach
Instrument Make/Model
Carbon Monoxide (CO)
nondispersive infrared
Model 48i-TLE Thermo Scientific
Oxides of nitrogen (NO/NO2/NOx)
chemiluminescence
Model 200A API
Carbon Monoxide (CO)
Quantum Cascade Laser
Aerodyne Research, Inc.
Nitrogen Dioxide (NO2)
Cavity Attenuation Phase Shift
Aerodyne Research, Inc.
Particle number concentration (size range 5.6-560 nm, 32 channels)
Engine Exhaust Particle Sizer
TSI, Inc., Model 3090
Sample Type and Frequency Fixed-Site Reference Continuous 1-minute Fixed-Site Reference Continuous 1-minute Fixed or Mobile Research 1-second Fixed or Mobile Research 1-second Fixed or Mobile Research 1-second
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Measurement Parameter
Sampling Approach
Instrument Make/Model
Black Carbon (BC)
Single-channel Aethalometer
Magee Scientific, AE-51
Nitrogen Dioxide (NO2)
Electrode Sensor
CairPol CairClip
Longitude and Latitude
Global positioning system
Crescent R100, Hemisphere GPS
Video
Webcam
Wind speed and direction
sonic anemometer
RM Young 81000
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Sample Type and Frequency Fixed or Mobile Sensor 1-second/ 1-minute Fixed or Mobile Sensor 1-second/ 1-minute Fixed or Mobile Research 1-second Fixed or Mobile Research 1-second Fixed-Site Research 1-second
3. Case Studies: Urban Built and Green Infrastructure Impacts on Air Quality Experiments have been conducted in the United States to investigate how roadside features, particularly solid noise barriers and roadside vegetation, can be used to improve local air quality and reduce air pollution exposures for the nearby population. An understanding of the impacts of these urban features required the use of reference and research-grade monitors, low-cost sensors and mobile monitoring. For a study in Raleigh, North Carolina, USA, a combination of mobile monitoring was integrated with fixed reference and low-cost sensor measurements to understand how a solid noise barrier with and without vegetation affected near-road pollutant concentrations compared to a similar location with no obstructions to air flow (Baldauf et al., 2008; Cho et al., 2009). Figure 1 shows an overview of the sampling site with reference method samplers housed in the trailer, as well as fixed, low-cost sensors located at increasing distances away from the road. Figure 2 shows temporal measurements from the reference and low-cost sensor fixed sites, highlighting the variability of air pollution during the day. In particular, the figure shows that pollutant concentrations of many pollutants were elevated during the morning when traffic volumes were high and wind speeds were low and generally from the road due to morning inversions.
Figure 1. Integrated monitoring at the near-road clearing site. Reference method and meteorological samplers were located in the shelter to the right. Low-cost, portable samplers were located on the poles in the model to form a
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transect away from the road. Mobile monitoring occurred with the vehicle to the left, shown parked in this picture but also collected samples while driving.
Figure 2. Real-time a) wind speed and direction, b) nitrogen oxide (NO), c) carbon monoxide (CO), d) ammonia, e) benzene and f) napthalene measured with reference and research-grade samplers at the near- road site shown in Figure 1 (Baldauf et al., 2008a). To assess the impact from the solid noise barrier with and without vegetation, mobile monitoring was required since the area of interest was a residential neighbourhood where large, reference samplers could not remain during the day and low-coast sensors could not be observed or accessed due to security concerns. However, the reference method and low cost fixed sampler data was used to determine that morning hours were the best to conduct the mobile monitoring due to the high impacts. In addition, the mobile monitors were parked next to the fixed monitors at the beginning and end of each sampling day for quality assurance analyses. Figure 3 shows the results of the mobile monitoring. The results indicated that the solid noise barrier reduced near-road pollution concentrations by 15-20 percent. When vegetation was directly behind the solid
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barrier, the pollutant levels were reduced even more, as much as 50 percent lower than a similar distance from the highway with no obstruction to air flow.
Figure 3. Mobile monitoring data collected along a sampling route that encompassed the clearing site shown in Figure 1, a behind noise barrier only site, and a behind barrier with vegetation site. The results highlight the reduction in downwind air pollution behind the noise barrier, with a further decrease behind the barrier with vegetation (Baldauf et al., 2008b).
A similar study was conducted in Phoenix, Arizona, USA to evaluate how solid noise barriers impact nearby, urban air quality. This study, also conducted in a residential area, utilized low-cost sensors and mobile monitoring since reference method samplers could not be located in the study area due to site space restrictions and lack of external power. Given these restrictions, fixed, low cost sensors were placed behind the noise barrier and in a clearing location to determine how pollutant concentrations varied over time. Figure 4 shows a monitoring tower placed behind the barrier and in the clearing, collecting air quality and wind speed/direction data at multiple heights. Mobile monitoring along residential roads traversing the clearing and behind barrier areas provided data on how
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pollutant concentrations varied by distance both behind the barrier and in a clearing with no obstructions to air flow. Figure 5 shows that concentrations were lower behind the barrier as far as 300 meters from the highway, with reductions as high as 50 percent within the first 50 meters of the barrier. While reference sampling was not conducted on-site, EPA did maintain a reference method, NAAQS air quality station approximately 5 km from the study area. Both the mobile and fixed sensors were collocated with the NAAQS station before, during and after the field study for quality assurance assessments.
Figure 4. Aerial view of the Phoenix Noise Barrier Study site with fixed, low-cost sensor locations shown by pins and the mobile monitoring route shown in blue (clearing area) and yellow (behind barrier area). A picture of the noise barrier is shown to the right.
Figure 5 – Left, Nitrogen dioxide (NO2) and right, ultrafine particle (UFP) measurements on the highway and at varying distances from the road in the clearing and behind the solid noise barrier. The distributions show the mean (dot), median, 75/25th percentiles and 95/5th percentiles. All differences are significant with the exception of the on-highway distributions (Baldauf et al., 2016).
A recent study in the San Francisco, California area investigated how roadside vegetation alone impacts nearby urban air quality. As with the study in Phoenix, lack of power and sufficient space prohibited the use of reference method sampling at the study location. As a result, a combination of mobile and fixed, low cost sensors measured air quality in the area, comparing downwind pollutant concentrations in a clearing with no obstructions to air flow with measurements behind roadside vegetation with varying characteristics. Five sites with differing vegetation type, height, thickness and porosity were chosen to identify how these parameters affect downwind pollutant concentrations. Figure 6 shows a picture of the monitoring location and fixed, sensor towers measuring air quality and meteorological parameters. In addition, mobile measurements were collected along the access road parallel to
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the highway with and without roadside vegetation to identify spatial changes in air quality. In addition to continuous driving, the mobile monitoring platform stopped at each site for approximately 10-minutes each day to collect more time resolved data. A second mobile monitoring vehicle remained parked at the clearing site for comparisons. As shown in Figure 7, different vegetation characteristics resulted in varying air pollution levels behind the vegetation. For dense, tall and thick vegetation, downwind air pollution concentrations reduced by more than 50 percent; however, highly porous vegetation with gaps resulted in negligible reductions for particulate pollutants and actually increased concentrations of gaseous pollutants. These results highlight characteristics of roadside vegetation needed for downwind pollutant reductions as seen in other studies as well (see reviews by Ajileth et al, 2017; Baldauf, 2017).
Figure 6. Locations of the vegetation barrier study with the clearing (1), behind bushes approximately 3m tall (2), behind porous bushes with gaps (3), behind thick bushes 4.5m tall (4), behind thick 4.5m bushes with 1m wide gap (5) and behind thick bushes 4.5m tall and trees 10m tall (6). Mobile monitoring vehicles are shown parked at sites (1) and (5) while the low-cost sensor and meteorological measurements are shown at site (3).
Figure 7. Distributions of nitrogen dioxide (NO2) and ultrafine particles (UFP) at each measurement site with the low-cost and mobile monitoring measurements. The distributions show the mean (dot), median, 75/25th percentiles and 95/5th percentiles. All differences are
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significant
4. Conclusions Ambient air quality monitoring techniques each provide unique strengths and limitations. Reference and research-grade instruments collect highly accurate and precise data, but require external power, temperaturecontrolled shelters and can be very costly. Low-cost sensors do not require external power and are inexpensive; however, these instruments are less accurate and reliable. Both sampling systems can provide important information on temporal changes in pollutant concentrations when operated at fixed locations. Mobile monitoring provides expanded spatial measurement capabilities by installing instruments in a moving platform such as a vehicle or on a person walking or biking. Each of these monitoring techniques was integrated for studies conducted in the United States evaluating how urban built and green infrastructure affects local, urban air quality. The results of these studies highlighted how solid noise barriers and roadside vegetation, individually or in combination, can improve local air quality when containing the proper characteristics. The studies also showed that some vegetation types and characteristics can degrade local air quality. Integrating the multiple measurement techniques provides an important mechanism to properly assessing local and urban air quality. These techniques can also be important in the design and evaluation of land use planning and urban infrastructure options for improving air quality. These measurement techniques also provide the data needed to develop and evaluate air quality dispersion models to estimate population exposures to pollution emissions and quantify the benefits of air pollution mitigation strategies including built and green infrastructure (e.g. Baldauf et al., 2017; Gallagher et al., 2015). Acknowledgements The author gratefully acknowledges the EPA and external team members who contributed to the field studies and results described in the case studies in Section 3 including all co-authors and acknowledged personnel in the references cited for each study. Disclaimer The views expressed in this publication are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.There is also the option to include a subheading within the Appendix if you wish. References Baldauf, R.W, (2017) Roadside vegetation design characteristics that can improve local, near-road air quality. Transportation Research Part D: Transport and Environment, 52, pp.354-361. Baldauf, R.W., V. Isakov, A. Venkatram, P. Deshmukh, B. Yang, K.M Zhang (2016) Influence of solid noise barriers on near-road and on-road air quality, Atmospheric Environment, Vol. 129, Pages 265–276. Baldauf, R.W., A. Khlystov, V. Isakov, E. Thoma, G.E. Bowker, T. Long, R. Snow (2008a) Impacts of Noise Barriers on Near-Road Air Quality, Atmospheric Environment. 42: 7502–7507. Baldauf, R.W., E. Thoma, M. Hays, R. Shores, J. Kinsey, B. Gullett, S. Kimbrough, V. Isakov, T. Long, R. Snow, A. Khlystov, J. Weinstein, F. Chen, R. Seila, D. Olson, M.I. Gilmour, S.H. Cho, N. Watkins, P. Rowley, J. Bang (2008b) Traffic and Meteorological Impacts on Near Road Air Quality: Summary of Methods and Trends from the Raleigh Near Road Study, J. Air & Waste Manage Assoc. 58:865–878 Baldauf, R.W., R.W. Wiener, D. Hiest, 2002, Methodology for Siting Ambient Air Monitors at the Neighborhood Scale, J. Air & Waste Manage Assoc, 52:1433-1442. Cho, S.H., H. Tong, J. McGee, R.W. Baldauf, T. Krantz, M.I. Gilmour (2009) Comparative Toxicity of Size-Fractionated Airborne Particulate Matter Collected at Different Distances from an Urban Highway. Environmental Health Perspectives, Vol, 117 (11). Gallagher, J., R.W. Baldauf, C.H. Fuller, P. Kumar, L.W. Gill, A. McNabola (2015) Passive methods for improving air quality in the built environment: A review of porous and solid barriers. Atmos. Environ. 120, pp. 61-70.
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Ghosh, J.K.C., J.E. Heck, M. Cockburn, J. Su, M. Jerrett, B. Ritz, (2013) Prenatal exposure to traffic-related air pollution and risk of early childhood cancers. American journal of epidemiology, 178(8), pp.1233-1239. Hagler, G.S.W., E.D. Thoma, R.W. Baldauf (2010) High-Resolution Mobile Monitoring of Carbon Monoxide and Ultrafine Particle Concentrations in a Near-Road Environment, J. Air & Waste Manage Assoc. 60: 328–336. U.S. Environmental Protection Agency (2017) Air Sensors Toolbox, https://www.epa.gov/air-sensor-toolbox U.S. Environmental Protection Agency (2012) Near-Road NO2 Monitoring Technical Assistance Document, EPA454/B-12-002, Office of Air Quality Planning and Standards, Research Triangle Park, NC.