Daily Patterns in PM2.5 Ion Concentrations in Claremont, California

ABSTRACT

Environmental scientists commonly study net air pollution levels, but rarely analyze the inorganic ionic composition of that pollution and the effect of weather conditions on that air pollution together. In order to determine the inorganic ion components of and weather condition effect on particulate matter with aerodynamic diameter less than 2.5 micrometers (PM2.5) in Claremont, California, we used a Particle-into-liquid-Sampler with Ion Chromatography (PILS-IC) system. This is the only study of its kind to employ the PILS-IC system in Claremont. A weather station connected to a PILS-IC system gathered weather data for the analysis. It was determined that the single weather variable that varies the most directly with pollution levels is wind speed. Surprisingly, temperature and humidity do not correlate nearly as well with pollution concentrations. This study concludes that the presence or absence of an inversion layer is the single most important factor in determining PM2.5 levels. Studies of this type could be extremely valuable in determining the sources of air pollution and analyzing how weather conditions affect air pollution levels.

INTRODUCTION

Particulate matter is a solid or liquid particle suspended in air (Seinfeld and Pandis 1998). Particulate matter air pollution is separated into three size groups: PM10 (Course - less than 10 μm in aerodynamic diameter), PM2.5 (Fine- less than 2.5um aerodynamic diameter), and "ultrafine" particulate matter (smaller than 0.1 μm in aerodynamic diameter). These particles are primarily composed of sulfate ions, organic carbon, black carbon, and nitrate ions, all emitted from combustion processes, although other ions can appear in significant quantities at times.

A particle into Liquid Sampler-Ion Chromatograph (PILS-IC) was used to measure PM2.5 concentrations. PILS-IC systems are unique and useful. Most traditional air pollution monitoring systems simply measure the gross quantity of particulate matter pollution in the atmosphere over a long period of time, but do not differentiate the particulate matter by its composition (Drewnick et al. 2003). Other methods lack the ability to determine when pollutant levels varied throughout a day, such as a Gorin, Collett, and Herckes, which use filters to take an average sample over the course of an entire day (Gorin et al. 2006). The PILS-IC system is capable of determining particulate composition every 15 minutes, solving the problems of both other systems. Research with the PILS-IC led to the discovery of potassium as a tracer of biomass combustion. This was determined when an aircraft equipped with a PILS-IC was flown through smoke plumes, an instance that clearly demands a fast analysis time resolution (Ma et al. 2003).

Air pollution is a serious issue in the Los Angeles Basin. Together, the Los Angeles-Orange-Riverside counties rank as the most polluted region in multiple categories, including most polluted year-round and short term by PM2.5, according to the American Lung Association (American Lung Association 2007). Increased levels of particulate matter pollution can be deadly to humans during serious episodes by aggravating existing health problems through inflammation of respiratory tissues (Li et al. 2003). A serious air pollution episode in London in 1953 resulted in the deaths of 4,000 to 12,000 people (Bell and Davis 2001). Particulate matter can also damage the lung capacity of individuals who are exposed during childhood and adolescence, significantly decreasing their lung capacity through long-term exposure (Gauderman et al. 2004).

Inversion layers play a significant role in determining pollutant levels in Claremont. An inversion layer is a zone of air near the ground that is colder than the air above it. Because of the temperature and density difference, air in inversion layers does not interact and mix with the rest of the atmosphere above it. The topography of the Los Angeles Basin, with the ocean to the west and mountains ringing the basin to the north, east, and south, is ideal for the formation of an inversion layer because pollutants are trapped by the mountains (Morris 2006). Inversion layers play a serious role determining air pollution levels by trapping pollutants emitted from ground-based sources and preventing them from escaping to other parts of the atmosphere (Morris 2006; Chow et al. 1994). This experiment sought to explore what relationship existed between weather conditions and PM2.5 levels in Claremont, California using the PILS-IC system discussed above.

MATERIALS AND METHODS

The Particle-into-Liquid Sampler (PILS) and Ion Chromatography (IC) System were used to measure the concentration of ions present in the atmosphere. The PILS-IC campaign was carried out during three separate sampling periods: October 2-9, 2005, March 15-24, 2006, and June 7-21, 2006. The weather data was collected during the same periods.

PILS-IC System

The PILS-IC system is used to collect and analyze the water soluble inorganic fraction of fine particulate matter. An air pump attached to the PILS pulls air through the inlet line and a cyclone on the roof of the W.M. Keck Science Center in Claremont, California. The cyclone only allows aerosols with an aerodynamic diameter of 2.5 μm or less through the inlet line. The air is pulled through two denuders, one coated with citric acid solution (0.080 M glycerol, 0.20 M citric acid, chemicals from Aldrich Chemicals) and the other with sodium bicarbonate (0.012 M glycerol, 0.00015 M sodium bicarbonate, chemicals from Aldrich Chemicals). The denuders remove any reactive gases in the air sample, such as HNO3, SO2, and NH3, that may react with the aerosols. This prevents positive artifacts in the data due to gases dissolving in the water sample (Weber et al. 2001). The air then enters the body of the particle growth chamber where the particles are hydrated with steam at approximately 250°C. Hydrating the particles makes them larger, growing them from less then 2.5 micrometers to 1-10 micrometers (Seinfeld and Pandis 1998). They collide into a quartz impactor and are washed off with LiBr water. The solution passes through a "de-bubbler", a joint with a slight widening which allows air bubbles to be pumped out of the flow to prevent any interference with the ion chromatography system. Another joint splits the solution so that it flows into both IC's, where the anions and cations are analyzed separately. The solution fills a 500 μL sample loop at a rate of 0.03 mL/min so that the loop fills up completely in 15 minutes. The contents of the sample loop are injected into the chromatography columns every 15 minutes where the ions are separated. Eluent (cation eluent: 4mmoll Tartaric Acid / 0.75mmol dipicolinic acid, anion eluent: 3.2mmol Sodium Carbonate / 1.0mmol Sodium Bicarbonate, all chemicals from Aldrich Chemicals) is also run through the columns. As the liquid exits the column, it is monitored by a conductivity meter and the data collected is processed using the ICNet 2.3 software program. The columns used were the Metrosep A SUPP 5 100 and C2 100 columns.

Because the aerosols are present in minute concentrations, the water used to make the eluent and LiBr solutions must be ultra-pure to ensure that the solutions are not contaminated by ions previously present in the water. A Millipore water system was used to filter de-ionized water. This 18.2 MΩ water was then used to make all solutions.

Lithium bromide water was used as an internal check. A known concentration of LiBr water is run across the PILS impactor plate and through the IC columns. The levels of LiBr being measured by the column are compared to the known concentration of our LiBr water, because that solution is the only source of lithium and bromide in the system. The loss of lithium and bromide caused by the debubbler is quantified and the levels of the other pollutants are then adjusted to reflect this loss accordingly.

Weather Station

A Davis "Weather Monitor II" Weather Station was used to monitor weather variables during the experimental runs. The station measured temperature, humidity, wind speed, and wind direction and was located on the roof of the Keck Science Center alongside the PILS-IC inlet.

RESULTS

Monthly Comparison

As evidenced by Figure 1, March and October had very similar PM2.5 patterns, while June had approximately 60% the levels of the other two months. The differences between the PM2.5 levels will be investigated with regard to weather variables in order to determine what weather conditions cause high levels of PM2.5.

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Weather Analysis

Four weather variables, humidity, temperature, wind speed, and wind direction were analyzed in search of a connection between the weather and PM2.5 levels.

Humidity and Inorganic PM2.5

Figure 2 shows no obvious and direct correlation between the level of relative humidity and the quantity of inorganic PM2.5. June and March had equal levels of humidity, but March had almost twice as much PM2.5. October is about 15% less humid on average, but had a level of PM2.5 comparable to March. There is a correlation between humidity and pollution levels in that PM2.5 pollution tends to reach its daily minimum near the daily minimum level of humidity. Humidity can be indicative of pollution levels if it is not considered as its own measurement, but rather as an indicator of the presence of an inversion layer. As will be discussed later, the peak humidity times correlate well with the times when inversion layers appear to be present, which can help to explain the PM2.5 level peak times.

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Temperature and Inorganic PM2.5

As evidenced by Figure 3, there is also no direct relationship between temperature and PM2.5 levels on an hour-by-hour basis. Peak temperatures occur between 1 pm and 3 pm, while peak pollution levels occur around 10 am. As temperature increases throughout the day, the concentration of PM goes down. The monthly comparisons also show no direct correlation between PM2.5 levels and monthly temperatures. In March, temperatures about half as high as those of June and October produced a concentration of inorganic PM2.5 comparable to October. June, although with a temperature nearly equivalent to that of October, had an average pollution level far below that of October. Averaged hour-by-hour analyses show that time periods of high temperatures do not necessarily correspond to time periods of decreased levels of PM2.5. For instance, the temperature maximum occurs at 2:00 pm, whereas the maximum level of PM2.5 occurs at 10:00 am. Also, the lowest temperatures occur around 4:00 am, whereas the lowest PM2.5 levels occur around 4 pm. One would believe that the higher the temperature, the lower the PM concentration because more of the pollutants are in the gas phase, but this does not appear to be the only cause of PM2.5 level variation.

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Wind Speed and PM2.5

Wind speed is unique in this set of weather variables because it is very similar between the different months that were sampled. According to Figure 4, wind speed does correlate inversely with inorganic PM2.5 levels. The wind speed maximum occurs between 3:00 pm and 4:00 pm, just as the PM2.5 concentrations reach their minimum levels. The presence of a strong wind is indicative of the dispersion of the Los Angeles Basin inversion layer, which is present on the majority of days in October, March, and June (Morris 2006). This breaks the barrier that keeps cold air near the surface of the earth and allows vertical and horizontal mixing, dispersing pollutants across a much wider area and thereby significantly reducing pollution levels. This effect will be explored fully later.

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Wind Direction and PM2.5

As evidenced by Figures 5 and 6, there is not enough variation in the data to judge whether or not wind direction has an effect. Strong winds only occurred consistently when wind was blowing in a generally northern direction (between 0 and 40 degrees). Without any significant other wind to compare against the northeastern wind, it is not possible to find a correlation between wind direction and pollutant levels.

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DISCUSSION AND CONCLUSIONS

Wind speed is the single weather variable measured that can account for the differences in PM2.5 levels observed. Wind speed is an excellent indicator of inversion layer presence, and the presence of an inversion layer is the single most important factor in determining the concentration of PM2.5. The daily cycle of inversion layer formation and dissipation can be observed from the averaged daily plots.

Cold temperatures and high levels of humidity from about 8 pm to sunrise at around 6 am indicate that the inversion layer has formed. When the morning rush hour occurs between 7 am and 10 am, pollution levels increase as everything that is emitted from combustion engines is trapped in the inversion. Meanwhile, heating caused by the sun begins to break down the inversion layer. As the ground-level temperature rises, the humidity begins to fall. Throughout this period, the wind has begun to increase from the stagnant levels experienced overnight and slow breezes from between 6:00 am and 10:00 am steadily increasing in strength.

By 10:00 am, the wind is averaging 0.4-0.8 meters/second. Pollution levels begin to drop as pollutants are dispersed by this air movement. PM2.5 concentrations still remain relatively high, though, because the inversion layer is still somewhat intact. Pollution levels steadily decrease from their morning maximum as the inversion layer is broken up. Temperatures continue to climb, humidity continues to fall, and wind speed steadily increases.

Between 3:00 pm and 4:00 pm, wind speed reaches a maximum and pollutant levels reach a minimum. The inversion layer has been dispersed by the wind at this point, and pollutants are distributed throughout a large area due to vertical mixing and horizontal travel (Chow et al. 1994). The inversion layer begins to reform in the late afternoon, as temperatures and wind speeds drop while humidity rises. Interestingly, the pollution level does not reach its morning maximum, despite the fact that presumably the same amount of traffic occurs in the afternoon as had occurred in the morning (assuming those who commute in the morning must return in the evening). This is because the pollutants released in the evening are not trapped in an inversion layer, but instead are blown throughout a large area by the winds that are prevalent at that time.

This cycle is supported by the findings of Chow et al., who studied pollutant concentrations in the Eastern Los Angeles Basin and concluded that pollutant maxima occur while the inversion is present between midnight and 10:00 am and pollutant minima occur when the wind speed is high and the inversion layer is absent between 4 pm and 6 pm (Chow et al. 1994). Also, Choi and Speer find that an inversion layer causes pollutant effects similar to this in the Seoul, South Korea region, which has geographic characteristics similar to the Los Angeles Basin (Choi and Speer 2006). In their paper, pollutant levels also closely mirror the presence or absence of an inversion layer throughout the day.

While the data looks conclusive, further experiments need to be done. Data was only taken over four weeks during one year. Further data collection during other seasons will be needed to further prove that inversion layers are indeed trapping the water soluble inorganic component of fine particulate matter. In future work, we would like to further study how the chemical interactions of the individual ions measured, ammonium, sulfate, nitrate, etc. are impacted by varying weather conditions. This type of research can aid in understanding the chemistry of air pollution in the Southern California Air Basin, and lead to potential mitigation techniques for cleaning up the air.

ACKNOWLEDGEMENTS

This paper is in memory of William Wagner who was a junior at Claremont McKenna College and passed away shortly after the research was completed. We would like to thank to the W.M. Keck Foundation for providing funding for this project. We would also like to thank Boyle Ke for his constant support and assistance with technological issues. Finally, we would like to thank the tech-support staff at Metrohm-Peak Inc. for help with the Ion Chromatographs.

REFERENCES

American Lung Association (2007) State of the Air Report. New York: ALA, pp. 19-27.

Bell, M.L. and D.L. Davis (2001) Reassessment of the Lethal London Fog of 1952: Novel Indicators of Acute and Chronic Consequences of Acute Exposure to Air Pollution. Environmental Health Perspectives 109, 389-394.

Choi, H. and M.S. Speer (2006) Effects of atmospheric circulation and boundary layer structure on the dispersion of suspended particulates in the Seoul metropolitan area. Meteorology and Atmospheric Physics 92, 239-254.

Chow, J.C. et al. (1994) Temporal and Spatial Variations of PM2.5 and PM10 Aerosol in the Southern California Air-Quality Study. Atmospheric Environment 28, 2061-2080.

Drewnick, F. et al. (2003) Intercomparison and evaluation of four semi-continuous PM2.5 sulfate instruments. Atmospheric Environment 37, 3335-3350.

Gauderman, W. et al. (2004) The Effect of Air Pollution on Lung Development from 10 to 18 Years of Age. The New England Journal of Medicine 351, 1057-1067.

Gorin, C.A. et al. (2006) Wood smoke contribution to winter aerosol in Fresno, CA. Journal of the Air and Waste Management Association 56, 1584-1590.

Li, N. et al. (2003) Particulate Pollutants Induce Oxidative Stress and Mitochondrial Damage. Environmental Health Perspectives 111, 455-460.

Ma, Y. et al. (2003) Characteristics and influence of biosmoke on the fine-particle ionic composition measured in Asian outflow during the Transport and Chemical Evolution Over the Pacific (TRACE-P) experiment. Journal of Geophysical Research-Atmospheres 108, 8816.

Morris, T.R. (2006) The Climate of Los Angeles, California. Oxnard, CA: National Weather Service Forecast Office, pp. 1-40.

Seinfeld, J.H. and S.N. Pandis (1998) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. New York: John Wiley & Sons, pp. 97-103.

Weber, R. J. et al. (2001) A particle-into-liquid collector for rapid measurement of aerosol bulk chemical composition. Aerosol Science and Technology 35, 718-727.

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