Use of Low-Cost Ambient Particulate Sensors in Nablus, Palestine with Application to the Assessment of Regional Dust Storms

Few air pollutant studies within the Palestinian territories have been reported in the literature. In March–April and May–June of 2018, three low-cost, locally calibrated particulate monitors (AirU’s) were deployed at different elevations and source areas throughout the city of Nablus in Northern West Bank, Palestine. During each of the three-week periods, high but site-to-site similar particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) and less than 10 µm (PM10) concentrations were observed. The PM2.5 concentrations at the three sampling locations and during both sampling periods averaged 38.2 ± 3.6 µg/m3, well above the World Health Organization’s (WHO) 24 h guidelines. Likewise, the PM10 concentrations exceeded or were just below the WHO’s 24 h guidelines, averaging 48.5 ± 4.3 µg/m3. During both periods, short episodes were identified in which the particulate levels at all three sites increased substantially (≈2×) above the regional baseline. Air mass back trajectory analyses using U.S. National Oceanic and Atmospheric Administration’s (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model suggested that, during these peak episodes, the arriving air masses spent recent days over desert areas (e.g., the Saharan Desert in North Africa). On days with regionally low PM2.5 concentrations (≈20 µg/m3), back trajectory analysis showed that air masses were directed in from the Mediterranean Sea area. Further, the lower elevation (downtown) site often recorded markedly higher particulate levels than the valley wall sites. This would suggest locally derived particulate sources are significant and may be beneficial in the identification of potential remediation options

Determination of Particle (PM10 and PM2.5) and Gas-Phase Ammonia (NH3) Emissions from a Deep-Pit Swine Operation Using Arrayed Field Measurements and Inverse Gaussian Plume Modeling

The contribution of agricultural emissions of primary (direct) and secondary (precursor) pollutants to air quality is rapidly being recognized as an important fraction of local and regional air pollution budgets. However, a significant uncertainty still exists in the magnitude and rate of these types of emissions, especially under “in field” conditions common within the central and western United States. Described herein are the results of a study conducted at a deep-pit swine production facility in central Iowa. The facility consisted of three separate, parallel barns, each housing around 1,250 pigs with an average weight of approximately 90 pounds per animal. The area around the facility was topographically flat and surrounded by soybean and cornfields. A number of portable PM10/PM2.5 (AirMetrics MiniVol) samplers and passive NH3 (Ogawa Model 3300) samplers were arrayed vertically and horizontally around the three-barn production facility, and data were collected on a daily-averaged basis for approximately three weeks in August and September of 2005. Additionally, a monitoring station was established approximately 40 m to the north of the nearest barn to record the typical suite of meteorological parameters (wind speed, direction, temperature, etc.) for determination of near-source atmospheric advection and dispersion. The AirMetrics samplers were operated with PM2.5 impactor separation heads for approximately the first half of the field study and were then switched to the PM10 heads for the remaining portion of the study. Each AirMetrics sampler was fitted with a conditioned, preweighed Teflon filter and operated at approximately five liters per minute for a time-controlled 23-hour period. Following sampling, the filters were recovered, conditioned, and reweighed at USU’s Utah Water Research Laboratory (UWRL) in Logan, UT for filter catch and ultimate determination of each location’s PM2.5/PM10 mass concentration. The Ogawa passive samplers were co-located and operated for the same time periods with the pre-treated (acid-coated) collection pads recovered after the same 23-hr period and stored appropriately until the final analysis for NH3 concentrations could be performed via ion chromatography at the UWRL facility. Emission estimates were derived via the comparisons of the measured particulate and NH3 concentrations at each sampling location with the concentrations for each receptor (sample) point found via application of the EPA-recommended ISCST3 air dispersion model (Lakes Environmental Software). The comparison of the measured and model predicted NH3 concentrations resulted in a derived NH3 emission rate of 17.22 ± 7.2 g/pig/day. This value is slightly more than two times greater than referenced emission rates; however, the two emission rates are within statistical uncertainty of each other. The analyses for the particulate emissions are as yet incomplete; however, preliminary calculations show PM10 and PM2.5 emission rates of 0.55 and 0.14 g/pig/day, respectively

Multi Path FTIR Agriculture Air Pollution Measurement System

This paper details the design and validation of a Multiple Path OP-FTIR system with elevation and radial scanning ability and demonstrates its capabilities to quantify and monitor gaseous ammonia emitted from agricultural facilities. The OP-FTIR system has a 500 m range (1000 m full path length) and allows 360° radial scan and 45° scan in elevation. To study large scale sources, two or more similar systems may be needed. For comparison purposes, we ran two similar but not identical OP-FTIR systems side-by-side in a controlled lab environment and in a series of field environments. We determined that in a controlled environment, the two systems can attain an NH3 agreement of 1- 3% at concentrations above 500 ppb. Due to the short path length (~10 m) in the lab, 500 ppb was the detection limit of the two systems. Path lengths in a field are much longer, allowing a lower detection limit. Average agreement in the field was 1-6%. This difference in agreement from the laboratory is likely due to the non-homogeneous distribution of the pollutant

Aglite: A 3-Wavelength Lidar System for Quantitative Assessment of Agricultural Air Quality and Whole Facility Emissions

Ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) are increasingly being used to characterize ambient aerosols due to key advantages (i.e., wide area of regard (10 km2), fast response time (s-1), high spatial resolution (\u3c10 \u3em) and high sensitivity). Scanning lidar allows for 3D imaging of atmospheric motion and aerosol variability, which can be used to quantitatively evaluate particulate matter (PM) concentrations and emissions. Space Dynamics Laboratory, in conjunction with USDA ARS, has developed and successfully deployed a lidar system called Aglite to characterize PM in diverse settings. Aglite is a portable scanning elastic lidar system with three wavelengths (355, 532, and 1064 nm), 6 m long range bins, and an effective range from 0.5 to 15 km. Filter-based PM samplers, optical particle counters, and various meteorological instruments were deployed to provide environmental and PM conditions for use in the lidar retrieval method. The developed retrieval algorithm extracts aerosol optical parameters, which were constrained by the point measurements, and converts return signals to PM concentrations. Once calibrated, the Aglite system can map the spatial distribution and temporal variation of the PM concentrations. Whole facility or operation-based emission rates were calculated from the lidar PM data with a mass balance approach. Concentration comparisons with upwind and downwind point sensors were made to verify data quality; lidar-derived PM levels were usually in good agreement with point sensor measurements. Comparisons of lidar-based emissions with emissions estimated through other methods using point sensor data generally show good agreement

Variations in Particle Composition and Size Distributions in and Around a Deep Pit Swine Operation

Agricultural facilities are the source of many types of particles and gases that can exhibit an influence on air quality. Emissions potentially impacting air quality from agricultural sources have become a concern for regulatory agencies such as the United States Department of Agriculture (USDA) and the Environmental Protection Agency (EPA). Particle mass concentration influences from agricultural sources can include both primary particles (direct emissions such as dust) and secondary particles (formed from gaseous precursors such as ammonia)

Emissions Calculated from Particulate Matter and Gaseous Ammonia Measurements from Commercial Dairy in California, USA

Emission rates and factors for particulate matter (PM) and gaseous ammonia (NH3) were estimated from measurements taken at a dairy in June 2008. Concentration measurements were made using both point and remote sensors. Filter-based PM samplers and optical particle counters (OPCs) characterized aerodynamic and optical properties, while a scanning elastic lidar measured particles around the facility. The lidar was calibrated to PM concentration using the point measurements. NH3 concentrations were measured using 23 passive samplers and 2 open-path Fourier transform infrared spectrometers (FTS). Emission rates and factors were estimated through both an inverse modeling technique using AERMOD coupled with measurements and a mass-balance approach applied to lidar PM data. Mean PM emission factors ± 95% confidence interval were 3.8 ± 3.2, 24.8 ± 14.5, and 75.9 ± 33.2 g/d/AU for PM2.5, PM10, and TSP, respectively, from inverse modeling and 1.3 ± 0.2, 15.1 ± 2.2, and 46.4 ± 7.0 g/d/AU for PM2.5, PM10, and TSP, respectively, from lidar data. Average daily NH3 emissions from the pens, liquid manure ponds, and the whole facility were 143.4 ± 162.0, 29.0 ± 74.7, and 172.4 ± 121.4 g/d/AU, respectively, based on the passive sampler data and 190.6 ± 55.8, 16.4 ± 8.4, and 207.1 ± 54.7 g/d/AU, respectively, based on FTS measurements. Liquid manure pond emissions averaged 5.4 ± 13.9 and 3.1 ± 1.6 g/m2/d based on passive sampler and FTS measurements, respectively. The calculated PM10 and NH3 emissions were of similar magnitude as those found in literature. Diurnal emission patterns were observed

Particulate-Matter Emission Estimates from Agricultural Spring-Tillage Operations Using LIDAR and Inverse Modeling

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

Hubble Space Telescope Ultraviolet Spectroscopy of Fourteen Low-Redshift Quasars

We present low-resolution ultraviolet spectra of 14 low redshift (z<0.8) quasars observed with HST/STIS as part of a Snap project to understand the relationship between quasar outflows and luminosity. By design, all observations cover the CIV emission line. Nine of the quasars are from the Hamburg-ESO catalog, three are from the Palomar-Green catalog, and one is from the Parkes catalog. The sample contains a few interesting quasars including two broad absorption line (BAL) quasars (HE0143-3535, HE0436-2614), one quasar with a mini-BAL (HE1105-0746), and one quasar with associated narrow absorption (HE0409-5004). These BAL quasars are among the brightest known (though not the most luminous) since they lie at z<0.8. We compare the properties of these BAL quasars to the z1.4 Large Bright Quasar samples. By design, our objects sample luminosities in between these two surveys, and our four absorbed objects are consistent with the v ~ L^0.62 relation derived by Laor & Brandt (2002). Another quasar, HE0441-2826, contains extremely weak emission lines and our spectrum is consistent with a simple power-law continuum. The quasar is radio-loud, but has a steep spectral index and a lobe-dominated morphology, which argues against it being a blazar. The unusual spectrum of this quasar resembles the spectra of the quasars PG1407+265, SDSSJ1136+0242, and PKS1004+13 for which several possible explanations have been entertained.Comment: Uses aastex.cls, 21 pages in preprint mode, including 6 figures and 2 tables; accepted for publication in The Astronomical Journal (projected vol 133

Comparative effectiveness study of patient‐reported outcomes after proton therapy or intensity‐modulated radiotherapy for prostate cancer

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106701/1/cncr28536.pd

A USCLIVAR Project to Assess and Compare the Responses of Global Climate Models to Drought-Related SST Forcing Patterns: Overview and Results

The USCLI VAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include: What are the mechanisms that maintain drought across the seasonal cycle and from one year to the next? What is the role of the leading patterns of SST variability, and what are the physical mechanisms linking the remote SST forcing to regional drought, including the role of land-atmosphere coupling? The runs were carried out with five different atmospheric general circulation models (AGCM5), and one coupled atmosphere-ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Nino/Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic Multi-decadal Oscillation (AMO), and a global trend pattern. One of the key findings is that all the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the U.S. tends to occur when the two oceans have anomalies of opposite sign. That is, a cold Pacific and warm Atlantic tend to produce the largest precipitation reductions, whereas a warm Pacific and cold Atlantic tend to produce the greatest precipitation enhancements. Further analysis of the response over the U.S. to the Pacific forcing highlights a number of noteworthy and to some extent unexpected results. These include a seasonal dependence of the precipitation response that is characterized by signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. Another interesting result concerns what appears to be a substantially different character in the surface temperature response over the U.S. to the Pacific forcing by the only model examined here that was developed for use in numerical weather prediction. The response to the positive SST trend forcing pattern is an overall surface warming over the world's land areas with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all the models