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Monitoring Spatial-Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution...

Monitoring Spatial-Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution...

Lu Liang, University of North Texas

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  1. Monitoring Spatial-Temporal Patterns of PM2.5 For Improved Understanding of Air

    Pollution Dynamics Using Portable Sensing Technologies Lu Liang U of North Texas
  2. Air pollution concentration is highly dynamic at the intra-urban scale

  3. Air quality: block-by-block insight Oakland, California Figure courtesy: EDF

  4. Prevailing wind direction Street Canyon Effect - Influence of urban

    morphology in the vertical plane
  5. Street Canyon Effect - Influence of urban morphology in the

    vertical plane Prevailing wind direction Leeward Windward
  6. Research Questions 1. TO GET A SPATIAL AND TEMPORAL UNDERSTANDING

    OF PM2.5 CONCENTRATION AT THE BLOCK SCALE. 2. 2. TO UNDERSTAND HOW WEATHER CONDITIONS, URBAN MORPHOLOGY, PROXIMITY TO EMISSION SOURCES AFFECT PM2.5 CONCENTRATION. 1. To get a spatial and temporal understanding of PM2.5 concentration at the block scale. 2. To understand how weather conditions, urban morphology, proximity to emission sources affect PM2.5 concentration.
  7. Study area Texas commission on environmental quality (TCEQ) PM2.5 monitoring

    stations Hydraulic fracturing Idling trucks Cement plants Construction
  8. Experiment design • Total path is 1.6 miles. • Sampling

    at: 7AM, 12AM, 3PM, 7PM • 2019.1.28 – 2019.2.8 Dylos DC1700 BATTERY OPERATED AQM GlobalSat DG-500 Data Logger & Bluetooth GPS Receiver
  9. PM2.5 spatio- temporal dynamics on UNT campus

  10. Urban morphology characterization Lidar-derived vertical profile LULC - horizontal profile

  11. = Wind wedge A new way to quantify the effects

    of urban morphology on air pollution along the wind direction.
  12. Building Footprint Vegetation Height Vegetation Footprint

  13. Panel Data Analysis Cross-Sectional data Time-Series data Time 3: Time

    1: P 1 P 2 P 3 P 4 P 5 Time 2: P 1 P 2 P 3 P 4 P 5 P 1 P 2 P 3 P 4 P 5
  14. Panel Analysis Results Estimate Std Error T value P value

    WEATHER CONDITION wind direction 1.25 0.54 2.30 * wind speed 12.90 1.54 8.37 *** wind gust -17.47 0.96 -18.11 *** Temperature 4.79 0.68 7.07 *** Dew point 6.25 0.69 9.06 *** PROXIMITY TO EMISSION SOURCES distance to minor roads -0.17 0.29 -0.60 distance to major roads 3.08 0.34 9.03 *** distance to bus stops 0.03 0.29 0.10 URBAN MORPHOLOGY Wind wedge vegetation footprint -6.64 0.80 -8.30 *** building footprint 1.21 0.61 1.98 * vegetation height 6.29 0.52 12.15 *** building height -11.38 0.93 -12.20 *** Circular buffer vegetation footprint 90.58 6.27 14.44 * building footprint -94.87 6.21 -15.28 * vegetation height -1.86 0.79 -2.36 * building height -1.61 1.55 -1.04
  15. Urban landscape Air quality At the block-level The effect of

    vertical urban morphology is more prominent than the horizontal plane. Wind direction and speed should be considered in accounting for urban morphology effects.
  16. None
  17. • With Urbanization Environmental pollution Health effects Socioeconomic features Urban&Air

    Urban&Health Health Inequity Environmental Justice We aim to achieve: Personal exposure assessment
  18. Environmental justice: (EPA definition): the fair treatment and meaningful involvement

    of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies.
  19. Disproportionate impacts in Minnesota

  20. 1) its performance has been tested by AQ-SPEC; 2) it

    has two exact same sensors that allow self-calibration; 3) all data are publicly available
  21. None
  22. Low-cost sensor calibration challenge Proposed three-tier calibration system

  23. \’;pkll…………………...-=pl jo bn bjkkl Thanks