Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg ·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg ·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.
Multiple studies have established a strong link between aerosols and health issues [
Traditionally air quality studies have used static sensors to gather measurements. This yields a single number representing the air quality of the entire region. Typically there is a lack of neighborhood-scale observations of airborne particulates, and many towns have no observations at all. To help address this issue, a machine learning approach was previously developed, by Lary et al. (2014) [
Lary et al. (2014) [
This project used a mobile sensor package to gather data throughout the city mounted 1.5 m (5') above the ground. The mobile platform provided the ability to gather data on a neighborhood scale, allowing for a better understanding of the representativeness of this single 10 km pixel value for the actual air quality gradient within the region. This paper will start with a brief review of the sensor package, followed by a look at the flow regimes, and then examine the PM2.5 concentration variability and the aerosol size distribution. Finally, the question “What is the appropriate spatial resolution required to accurately characterize the PM2.5 abundance at a neighborhood scale?” will be explored.
The instrumentation package included a Grimm NanoCheck 1365 particle spectrometer, a New Mountain Innovations NM150 Ultrasonic Weather Station, and an Arduino micro-controller. The NanoCheck 1365 particle spectrometer combines a Grimm 1109 aerosol-spectrometer with a Grimm 1320 nano-particle sensor allowing for a measurement range of 0.25 - 32 μm. The 1365 is a completely self-contained instrument utilizing a 14.4 V, 4.8 Ah Li Ion rechargeable battery pack for power and USB flash drive for data storage. Air is drawn into the instrument through the 1109 using an internal pump, with a flow rate of 1.2 l∙min−1, and then passed on to the 1320 through a pneumatic adapter block. The spectrometer was factory calibrated and was periodically sent back to the factory for recalibration. The 1109 has a 6 s sample-interval, while the 1320 runs a 10 s sample-interval.
The NM150 is a complete weather station with NIST calibrated sensors for measuring temperature, pressure and humidity as well as wind speed and direction using 4 ultra-sonic transducers. This sensor incorporates its own GPS receiver and compass allowing for true wind speed and direction calculations. The methods used in calculating temperature, pressure, humidity, and wind speed/direction are shown in
Variable | Method | Range | Accuracy |
---|---|---|---|
Temperature | Based on a negative temperature coefficient thermistor that measures the ambient air temperature. | 30˚C - 50˚C (22˚F - 122˚F) | ±15˚C (±2.7˚F) @ 2 knots (2.3 mph) wind speed |
Pressure | Measured using a temperature-compensated silicon piezoresistive pressure sensor. | 850 - 1150 mbar (25 - 34 in Hg) | ±1.5% |
Humidity | Measured with a capacitive cell humidity sensor. | 10% - 95% RH | ±4% |
Wind speed | Ultrasonic anemometer. | 0.5 - 99.5 knots (0.6 - 114.5 mph) | The greater of ±1 knot or ±4% (±1.1 mph) |
Wind direction | Ultrasonic anemometer. | 0˚ - 360˚ | ±1.5˚ |
Particle counts | Particle spectrometer (655 nm laser diode). | 0.25 - 32 μm in 32 size bins | ±3% |
The NM150 is controlled using and reports data in standard NMEA 0183 formatted text strings. An Arduino ATmega 2560 micro-controller was used to read and write these text strings and the serial stream from the NanoCheck 1365. This micro-controller had 256 KB of flash memory, ran at 16 MHz, and was easily pro- grammed using the Arduino programming language and development environment. Data received from the NM150 and the NanoCheck 1365 were stored on-board the arduino using an SD card reader and standard 2 GB SD card. Trey Kasling of Kasling Aircraft designed and 3D printed a window mount to attach the met sensor and particle spectrometer probe to the back window of a 2011 Volkswagen Jetta for the ground phase of the measurement campaign.
The meteorological context plays a pivotal role in the abundance of airborne particulates. The wind can carry particles from upwind sources, surface solar heating can dry out the ground and increase the availability of local dust sources, and humidity will determine the amount of moisture available that airborne particulates can absorb. Thus, the weather conditions throughout the region of interest during the data collections need to be examined.
To objectively characterize the meteorological regimes a self organizing map (SOM) was used. The SOM classified the meteorological data into 10 different classes, each class corresponding to a distinct flow regime, in order to perform appropriate summary statistics for each flow regime (i.e. to compare like with like). SOMs are a way of reducing the dimensionality of multi-dimensional data sets [
This is also illustrated when comparing the data gathered on May 23 and May 28 shown in
Mean | May 23, 2014 | May 28, 2014 |
---|---|---|
Primary wind direction (˚) | 180 - 190 | 30 - 40 |
Barometric pressure (mbar) | 1017.7 ± 0.7 | 1012.9 ± 0.6 |
Relative humidity (%) | 60.8 ± 2.6 | 52.6 ± 2.5 |
PM2.5 (μg∙m−3) | 19 ± 2.8 | 5.2 ± 0.8 |
Temperature (˚C) | 24.6 ± 0.7 | 25.1 ± 0.6 |
Wind speed (m/s) | 3.4 ± 1.1 | 3.2 ± 0.8 |
areas along the southern boarder and extending south of the region. There are four key types of sources affecting our region of interest: traffic, construction, industrial, and out of region (up-wind) sources.
The area marked “1” on the map is the University of Texas at Dallas campus. During the time of this campaign, construction of a new dormitory and parking garage was ongoing. The area marked “2” on the map depicts the construction site of a high-rise office building, and area “3” is the industrial sector in Garland, TX. This industrial sector contains several manufacturing, chemical, and food production facilities. All data was collected during daily 3-hour drives starting at area 1, driving to the northwest corner of the region of interest, and then crossing the region east to west/west to east, with a small detour in the northeast corner to gather high- resolution data in a residential area, until reaching the southwest corner. The route then continued along State Highway 75 and over to the northeast corner. The region was then twice traversed north to south/south to north before returning to area “1”. All collections where made between 11:00 and 14:00 Central Standard Time (CST).
The region of interest does not contain any air quality monitoring stations. However, the Texas Commission on Environmental Quality (TCEQ) does maintain a monitoring station approximately 3.5 km from the south- western edge of the region. This station does not report PM2.5 data, but does report PM10 data as a 24 hr average. For May, 2014 data for only four days are reported, two of them coinciding with data collections taken in this study. For May 17, 2014 TCEQ reported a 24 hour average PM10 concentration of 13 μg∙m−3. The mean PM10 concentration measured in the region of interest during this period was 13.2 μg∙m−3. For May 23, 2014 TCEQ reported a 24 hour average PM10 concentration of 21 μg∙m−3. The mean PM10 concentration measured in the region of interest during this period was 23.4 μg∙m−3.
To understand the spatial variability within the region of interest, knowledge is needed of how each of the four sources are distributed across the region. This knowledge can be acquired by using the SOM from the meteorological classification to understand how the meteorological data correlates to the PM2.5 concentration. There, the class with the highest PM2.5 concentration was class 6 characterized by a mean PM2.5 concentration of 14.1 ± 5.7 μg∙m−3, with a range of 4.4 μg∙m−3 to 47.8 μg∙m−3. Class 6 also contained a mean temperature of 25.9˚C ± 1.6˚C, with a range of 20.8˚C to 29.1˚C, a mean pressure of 1014.6 ± 3.2 mbar, with a range of 1007.4 mbar to 1020.0 mbar, a mean humidity of 46.6 ± 7.2%, with a range of 30.2% to 65.9%, a mean wind speed of 4.2 ± 1.4 m∙s−1, with a range of 0.9 m∙s−1 to 8.5 m∙s−1, and a mean wind direction of 176.5 ± 20.3˚, with a range of 188.0˚ to 122.1˚. The wind direction for this class is roughly out of the south, corresponding to area “3” on the map in
The hysplit model was used to run forward and backward air parcel trajectories across the region. This model is only useful on scales much larger than the region of interest in this paper. It does, however, give insight to where the air that was sampled came from and how it traveled across the region. For May 23, 2014, the wind was out of the south blowing across the industrial sector just south of our region of interest and continuing on to the north. For May 28, 2014 the wind was coming out of the northeast (a more rural area of the region) and continued on to the southwest. Notice from
Epoch | A | B | C | D | E | F | G |
---|---|---|---|---|---|---|---|
Length scale (km) | 6.6 | 5.4 | 1.5 | 0.7 | 1.66 | 2.6 | 2.3 |
Time since rain (days) | 16 | 1 | 2 | <1 | 1+ | 1 | 2 |
Mean wind direction (˚) | 168.9 ± 34.8 | 217.7 ± 34.6 | 176.9 ± 28.3 | 326.1 ± 44 | 148.4 ± 20.5 | 47.9 ± 30 | 34 ± 28 |
PM2.5 (μg∙m−3) | 20.5 ± 4.2 | 20.1 ± 4.2 | 6.1 ± 0.7 | 3.0 ± 0.5 | 13.7 ± 3.7 | 5.2 ± 0.8 | 7.6 ± 1.2 |
EPA class | Moderate | Moderate | Good | Good | Moderate | Good | Good |
Mean temp (˚C) | 25.6 ± 1 | 27.6 ± 1 | 26.3 ± 0.9 | 17.7 ± 0.8 | 24.3 ± 1.6 | 25.1 ± 0.6 | 27.6 ± 0.7 |
Mean pressure (mbar) | 1009.3 ± 0.7 | 1011.6 ± 0.7 | 1010.6 ± 0.6 | 1023.3 ± 0.7 | 1016.7 ± 1.6 | 1012.9 ± 0.6 | 1011.8 ± 0.7 |
Mean humidity (%) | 34 ± 2.5 | 47.5 ± 5 | 47 ± 2.5 | 10.9 ± 3.0 | 47.9 ± 7.4 | 52.6 ± 2.5 | 48.8 ± 2.3 |
Mean wind speed (m∙s−1) | 3.6 ± 1.6 | 3.8 ± 1.2 | 3.9 ± 1.3 | 3.8 ± 1.1 | 3.7 ± 0.5 | 3.2 ± 0.8 | 3.0 ± 0.8 |
Mean size (μm) | 0.29 | 0.31 | 0.3 | 0.3 | 0.3 | 0.29 | 0.29 |
Unlike the previous SOM classification, each epoch was dominated, in part, by the local rainfall as denoted by the icons above and below the central plots. The majority of the epochs consist of single data collections, the sole exception is epoch E. This epoch contains seven data collections. It also represents the longest consecutive days without rainfall during the collection period.
Variograms were examined for each epoch to objectively characterize the length scale (range) of the PM2.5 spatial variability, shown in
Throughout the campaign, considerable variability was not only observed across the drive during any given day, but also from one day to another, particularly if it had just rained.
The concentration decrease is better indicated by the histogram and frequency distributions of the PM2.5 concentrations shown in
The data collections were grouped by epoch, and the mean count for each epoch was plotted against the size bin shown in
AQI catagory | Index values | PM2.5 breakpoints (μg∙m−3) |
---|---|---|
Green good | 0 - 50 | 0.0 - 12.0 |
Yellow moderate | 51 - 100 | 12.1 - 35.4 |
Orange unhealthy for sensitive groups | 101 - 150 | 35.5 - 55.4 |
Red unhealthy | 151 - 200 | 55.5 - 150.4 |
Violet very unhealthy | 201 - 300 | 250.5 - 350.4 |
Purple hazardous | 301+ | 350.5+ |
12 through the morning of May 14. Epoch D, corresponding to the data collected on May 14, showed the lowest particle counts for the month. Epoch E corresponds to several days of no rain and shows a steady increase of particle counts ending with 3 days of slight rainfall totaling only 6.86 mm (0.27") on May 25 - 27. However even though the amount of rain was approximately half of what fell very quickly on May 8, epoch F shows a decrease in particle counts. This indicates that a quick rainfall may not wash particles out of the atmosphere. An extended period of rain is needed.
To objectively characterize the different types of size distribution observed, an SOM was used to classify the particle counts into 10 classes using each size bin as a variable, as seen in
This study used an approach of recursive subdivision to examine the spatial scales of the data. First, the median value of the entire 10 × 10 km measurement area was calculated. The area was then subdivided into a 2.5 × 2.5 km grid, then again down to a 1.25 × 1.25 km grid, and the median value of each grid section was calculated.
In order to determine the appropriate spatial scale for future measurements, variograms for each day were created. The length scale (range) for each day is presented in
The variograms were separated into 3 groups, based on range, and an average variogram for each group was
calculated. The first group, shown in
The short scale group consists solely of epoch D and may be considered an outlier. In this case, we might conclude that a long length scale is associated with high PM2.5 concentration. However, toward the end of epoch E, PM2.5 concentration (
Since the long scale group only has two epochs as members then perhaps both the short and long scales can be considered outliers. In this case a simple conclusion can be made that the appropriate spatial scale for future measurements is approximately 1.7 km. This may be good enough for the majority of studies.
Rain was a leading factor in affecting particle distributions. However, the overall pressure system had a greater affect on the spatial scales. Notice, in
A possible cause for the change in spatial scale sensitivity can be seen in the synoptic weather systems shown in
This study looked at the size distribution, in the size range 0.25 - 32 μm, and the spatial and temporal variability across a 100 km2 area encompassing parts of Richardson, and Garland, TX. This area represented 1 pixel of data in the satellite-based ground level PM2.5 concentration estimate of Lary et al. (2014) [
To objectively characterize the meteorological regimes, an SOM was used to classify the meteorological data into 10 different classes. Wind direction was determined to be the dominant factor in classifying the data. A second SOM was used to classify the size distribution by size bin. Both SOM classifications determined that the highest PM2.5 concentrations corresponded to periods when the wind was out of the south, the southerly met class had a mean PM2.5 concentration of 14.1 μg∙m−3, and the southerly size class had a mean PM2.5 concentra- tion of 20.86 μg∙m−3.
The lowest PM2.5 concentrations where observed after several consecutive days of rainfall where mean PM2.5 concentrations reached as low as 3.0 ± 0.5 μg∙m−3. Extended periods of rainfall were found to not only “cleanse” the air, but to leave the region with a more uniform PM2.5 concentration.
This study found that the resulting spatial scales calculated from data collected each day varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km with the majority of data falling in the 1.7 km range. To fully understand the relationship between spatial scale and weather systems a longer study on a larger scale needs to be conducted.
The data gathering method used in this paper deployed on a larger scale can help garner a greater under- standing of the neighborhood scale variability of PM2.5 concentrations. This can be accomplished by attaching the sensor package to postal or other daily driven vehicles across the country. The data could then be incorpo- rated into the satellite estimates to improve their accuracy and help with future models for air quality forecast- ing.
The authors greatly appreciate the support of NASA with research funding through award NNX11AL18G. The views expressed in this paper are those of the authors and do not necessarily represent the views of NASA.