Evaluation of Air Quality Models with Near-Road Monitoring Data. Task 4: Data Exploration

Transcription

1 Evaluation of Air Quality Models with Near-Road Monitoring Data Task 4: Data Exploration Texas A&M Transportation Institute

2 Task 4: Data Exploration Objective Near-road monitors are installed primarily close to major roadways for monitoring near-road concentration levels Understand conditions when relatively high near-road PM concentrations have been observed Quantitatively assess the associations between key factors Near-road concentration Traffic Meteorology Background concentration Focus on year 2016 data 2

3 Near-road Monitoring Sites AQS Number Site Name Address Pollutants Monitored NO x CO PM 2.5 Distance to Nearest Traffic Lane (m) Dallas LBJ Freeway 8652 LBJ Freeway X Houston SW Freeway 5617 Westward Avenue X Austin North I N IH 35 SVRD SB X San Antonio I IH 35 N X Fort Worth California Parkway North 1198 California Parkway North X X X Houston North Loop 822 North Loop X X x 15 Houston Near-Road Sites in Texas Ft Worth 3

4 Houston Site Parameters Used for Data Exploration Pollutants (CAMS 1052) PM 2.5 CO NO, NO 2, NO x Ambient Parameters (On-site) Temperature Wind direction Wind speed Peak wind gust Averaging Period 24-hrs (1-in-3 days) Hourly Hourly Hourly Meteorological Data (Off-site) Atmospheric Stability Hourly Traffic Data Volume Speed Hourly Background Ambient Monitors (PM 2.5 ) CAMS 1, CAMS 35, CAMS 416, CAMS 40 Hourly 4

5 Frequency Distribution Min= 1.2 (ug/m3) Median= 9.8 Mean= Max= th perc.= Min= 0 (ppm) Median= 0.4 Mean= 0.45 Max= th perc.=1.1 Min= -2.6 (ppb) Median= 11.2 Mean= 13.6 Max= th perc.= Min= -0.3 (ppb) Median= 12.9 Mean= 19.0 Max= th perc.= 84.9 Min= -4.9 (ppb) Median= 26.6 Mean= 32.7 Max= th perc.=

6 Concentration Roses Wind Rose CO NO 2 6

7 Background Concentration Relation between Near-road and Regional Concentrations Ambient monitors are installed primarily for regulatory compliance and community-exposure monitoring Regional concentrations are influenced by multiple factors related to meteorology, industrial sources, and regional transport etc. Literature shows near-road PM 2.5 concentration to be dominated by background regional levels (90-95%) Near road Concentration Does roadways account for majority of the Incremental Contribution? 7

8 Ambient Monitors * C8 was not considered due to a high number of missing records

9 Ambient Monitors Near-road 24hrs PM 2.5 is strongly correlated with background conc. CAMS1 CAMS35 There is a 1.47 µg/m 3 (17%) increment at C1052 compared to ambient monitors CAMS403 CAMS416 * 107 PM 2.5 days in

10 Traffic Activity PM 2.5 Concentration (ug/m 3 ) y = 2E-05x R² = PM 2.5 > 15ug/m 3 PM 2.5 (μg/m 3 ) measured at CAMS1052 vs AADT measured close to monitor C = C C35 (ug/m 3 ) AADT 8 y = 1E-05x R² = AADT Near-road increment C (C1052-C35) vs AADT Near-road and near-road increment 24- hr PM 2.5 are not strongly related to AADT 10

11 Traffic Activity vs Other Pollutants CO NO 2 *Time series plotted for highest 10 PM 2.5 days in 2016 CO NO 2 Hourly average CO and NO 2 is not strongly related to hourly traffic volumes 11

12 Meteorology Wind Rose Meteorological parameters evaluated include wind speed, wind direction, temperature, relative humidity and atmospheric stability Dots= Concentrations Levels, Quadrant ( ) = Wind Direction, Concentric circles (0-10mph) = Wind Speed CAMS 1052 C 12

13 Traffic Volume and Meteorology Wind Rose Although high conc. values are found along the prevailing wind direction, conc. values are not strongly related to traffic volume Dots= Concentrations Levels, Quadrant ( ) = Wind Direction, Concentric circles (0-250,000) = AADT CAMS 1052 C 13

14 Traffic Speed and Meteorology Wind Rose High conc. values relate to 50-60mph average speed Dots= Concentrations Levels, Quadrant ( ) = Wind Direction, Concentric circles (40-65mph) = Traffic Speed CAMS 1052 C 14

15 Predictive Tools Decision Tree Artificial Neural Networks Back-propagation Neural Network with 10 neurons in hidden layer Critical Parameters Critical Parameters for C corresponds to Wind Direction, Season and Traffic Speed C Background Conc. and Season C - Wind Direction and Traffic Speed Important to understand the data to evaluate associations 15

16 Study Findings Houston site does not appear to have high pollutant concentration (CO, PM 2.5, NO 2 ) at a frequency sufficient enough to violate the NAAQS. Near-road PM concentrations vary more by urban-level PM values rather than by AADT Near-road increment PM 2.5 ( C) found to be influenced by meteorology rather than by AADT On average, there is a 17% (1.47 µg/m 3 ) increment at near-road site compared to background site Resolution of PM 2.5 measured at 24hrs is a major limitation to explore further associations 16

17 Supplemental Material

18 Concentration Rose Wind Rose CO Concentration Rose CAMS

19 Concentration Rose Wind Rose NO 2 Concentration Rose CAMS 1052 NO 2 NO NO x 19

20 Land Use 20

21 Land Use 21

22 Traffic Activity: Trucks, FE-AADT 25 PM 2.5 Concentration (ug/m 3 ) y = x R² = PM 2.5 > 15ug/m 3 Average truck percentage is obtained from STARS counter located close to the monitor Number of Trucks 25 PM 2.5 Concentration (ug/m 3 ) y = 1E-05x R² = PM 2.5 > 15ug/m 3 Fleet-Equivalent AADT ((FE-AADT) is a single metric accounting for both traffic volume and fleet mix FE-AADT (Emissions) 22

23 PM 2.5 and Traffic Volume C = C C1 (ug/m 3 ) y = 4E-06x R² = CAMS 1 C = C C35 (ug/m 3 ) y = 1E-05x R² = CAMS 35 C = C C403 (ug/m 3 ) AADT y = 5E-07x R² = CAMS 403 C = C C416 (ug/m 3 ) AADT y = 1E-05x R² = CAMS AADT AADT 23

24 Meteorology (Wind speed and direction) 24

25 Meteorology (Atmospheric Stability) 25

26 Near-road, and Meteorology (WD, Temp) C1052 C Temperature Distribution CAMS 1052 C Dots: Concentrations Levels, Quadrant: Wind Direction, Concentric circles: Temperature 26

27 Near-road, and Meteorology (WD, Rel Humidity) C1052 Relative Humidity Dots: Concentrations Levels, Quadrant: Wind Direction, Concentric circles: Relative Humidity 27

28 All parameters 28

29 Artificial Neural Network (BPNN) ϴ x: features, w: weights, ϴ: bias, f: activation function Back-propagation Neural Network with 10 neurons in hidden layer Weights assigned from input-hidden layer proportional to parameter importance C1052 vs all parameters C vs all parameters CAMS1052 Parameter Importance Month 5.59 Day 4.03 Temperature 5.66 Relative Humidity 4.04 Traffic Speed 2.28 Traffic Volume 3.75 Pressure 5.04 Wind Direction 4.25 Wind Speed 4.27 WindClass 5.32 Season 3.54 CAMS C Parameter Importance Month 5.08 Day 5.73 Temperature 6.16 Relative Humidity 8.36 Traffic Speed 9.34 Traffic Volume 7.14 Pressure 5.53 Wind Direction 9.34 Wind Speed 7.87 WindClass 7.73 Season