Lidar-assisted measurement of PM10 emissions from agricultural tilling in California's San Joaquin Valley – Part II: emission factors

Holmén, Britt A.; James, Teresa; Ashbaugh, Lowell L.; Flocchini, Robert G.

doi:10.1016/s1352-2310(00)00519-7

Cited by 32 publications

(24 citation statements)

References 6 publications

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“…All measurements were made under actual field conditions. A combination of upwind/downwind source isolation and vertical profiling were used to quantify PM 10 and PM 2.5 concentrations, as described in Holmén et al [5], and shown in Figure 1. We collected ambient PM 10 and PM 2.5 on Teflon filters using IMPROVE samplers [6,7] for gravimetric and elemental analyses.…”

Section: Methodsmentioning

confidence: 99%

Elemental composition of PM₁₀ and PM_2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Carvacho¹,

Ashbaugh²,

Flocchini³

2006

WIT Transactions on Ecology and the Environment, Vol 99

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Fugitive dust emissions from soil are thought to constitute a large fraction of the PM 10 and PM 2.5 inventory in California's San Joaquin Valley (SJV) and other western air basins, especially during dry periods. The major sources of these emissions are paved and unpaved roads, construction sites, windblown dust, and agricultural activities. Furthermore, PM 10 and PM 2.5 are considered to be among the most harmful of all air pollutants. When inhaled these particles evade natural defenses of the respiratory system and lodge deep in the lungs causing serious health problems. Some heavy metals in small particles have the tendency to donate electrons and to form basic oxides. Biologically, many metals are essential to living systems and are involved in a variety of cellular, physiological, and structural functions. But at high doses, many metals become toxic. The route of exposure may affect the dose and the site where the metal concentrates, and thus the observed toxic effect.In California's San Joaquin Valley, agricultural operations are highly complex and potentially significant sources of PM 10 and PM 2.5 , especially during late summer and fall. A series of experiments was conducted to measure PM 10 and PM 2.5 emissions with traditional array sampling from agricultural operations in San Joaquin Valley. The elemental analysis of PM 10 and PM 2.5 collected in the field samples was conducted using Proton Induced X-Ray Emissions (PIXE), Proton Elastic Scattering Analysis (PESA) and X-Ray Fluorescence (XRF) analytical techniques available in our laboratory.The composition of PM 10 dust collected downwind of agricultural operations is different from the composition of the PM 2.5 dust collected at the same time. The smaller particles are enriched in sulphur and in heavy metals.

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Section: Methodsmentioning

confidence: 99%

Elemental composition of PM₁₀ and PM_2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Carvacho¹,

Ashbaugh²,

Flocchini³

2006

WIT Transactions on Ecology and the Environment, Vol 99

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“…The rangecorrected background-subtracted signals from each channel were also used to calculate the volume depolarization ratio δ v by applying (3). The next step is to compute the particle depolarization ratio δ p (depolarization ratio of the agricultural aerosols) by applying (8).…”

Section: Lidar Data Analysismentioning

confidence: 99%

“…Finally, a basic error propagation study has been carried out so as to assess the impact of absolute errors ΔK * and ΔR p which are considered the two dominating error sources in this study, on the estimated particle depolarization ratio (8). An extensive error treatment of the depolarization lidar problem has been presented in [36] and is out of the scope of the present work.…”

Section: Lidar Data Analysismentioning

confidence: 99%

“…Lidar systems can provide real-time aerosol measurements with high temporal and spatial resolution. In previous studies, nonpolarized lidar systems have been used to measure PM 10 emissions from land preparation operations [6][7][8][9] to determine vertical PM concentration profiles. Several lidar studies have also been carried out to estimate the PM emissions due to livestock housing.…”

Section: Introductionmentioning

confidence: 99%

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Polarization Lidar Detection of Agricultural Aerosol Emissions

et al. 2018

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Agricultural aerosol emissions can significantly impact human and animal health as well as the environment. Therefore, it is essential to adopt new sensing techniques for real-time monitoring these emissions in high temporal and spatial resolution. In recent years, light detection and ranging (lidar) technology has been used for measuring the particulate matter emitted from agricultural operations. However, conventional nonpolarized lidar systems cannot discriminate between different types of aerosols, which can lead to misinterpretation of the results. To overcome this limitation, this study applies the polarization lidar technique to monitor agricultural aerosols. A 355 nm polarization lidar system was used to measure the emissions generated during pesticide spraying operations. The results showed that depolarization ratios due to field dust (0.220-0.268) and to road dust (0.385) are clearly higher than those caused by pesticide spray drift (0.028-0.043) or by diesel exhaust (0.099), which can be used to differentiate each type of aerosol. These results support the development of new polarization lidar systems specifically designed to study the impact of agricultural activities on air quality.

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“…Previous agricultural-tillage PM emissions studies [5][6][7][8][9][10][11][12] have focused almost exclusively on measuring PM 10 emission rates (ERs) and emission factors (EFs) from conventional tillage operations. For this discussion, EFs are emissions based on a quantity of production (e.g., g · m −2 ) and ERs are emissions that include a time factor (e.g., g · m −2 · s −1 ).…”

Section: Introductionmentioning

confidence: 99%

Particulate-matter emission estimates from agricultural spring-tillage operations using LIDAR and inverse modeling

Moore

Wojcik

Martin

et al. 2015

J. Appl. Remote Sens

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Abstract. Particulate-matter (PM) emissions from a typical spring agricultural tillage sequence and a strip-till conservation tillage sequence in California's San Joaquin Valley were estimated to calculate the emissions control efficiency (η) of the strip-till conservation management practice (CMP). Filter-based PM samplers, PM-calibrated optical particle counters (OPCs), and a PM-calibrated light detection and ranging (LIDAR) system were used to monitored upwind and downwind PM concentrations during May and June 2008. Emission rates were estimated through inverse modeling coupled with the filter and OPC measurements and through applying a mass balance to the PM concentrations derived from LIDAR data. Sampling irregularities and errors prevented the estimation of emissions from 42% of the sample periods based on filter samples. OPC and LIDAR datasets were sufficiently complete to estimate emissions and the strip-till CMP η, which were ∼90% for all size fractions in both datasets. Tillage time was also reduced by 84%. Calculated emissions for some operations were within the range of values found in published studies, while other estimates were significantly higher than literature values. The results demonstrate that both PM emissions and tillage time may be reduced by an order of magnitude through the use of a strip-till conservation tillage CMP when compared to spring tillage activities.

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Lidar-assisted measurement of PM10 emissions from agricultural tilling in California's San Joaquin Valley – Part II: emission factors

Cited by 32 publications

References 6 publications

Elemental composition of PM₁₀ and PM_2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Elemental composition of PM₁₀ and PM_2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Polarization Lidar Detection of Agricultural Aerosol Emissions

Particulate-matter emission estimates from agricultural spring-tillage operations using LIDAR and inverse modeling

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Lidar-assisted measurement of PM10 emissions from agricultural tilling in California's San Joaquin Valley – Part II: emission factors

Cited by 32 publications

References 6 publications

Elemental composition of PM10 and PM2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Elemental composition of PM10 and PM2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Polarization Lidar Detection of Agricultural Aerosol Emissions

Particulate-matter emission estimates from agricultural spring-tillage operations using LIDAR and inverse modeling

Contact Info

Product

Resources

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Elemental composition of PM₁₀ and PM_2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley

Elemental composition of PM₁₀ and PM_2.5 in ambient air downwind of agricultural operations in California’s San Joaquin Valley