Sources and Secondary Production of Organic Aerosols in the Northeastern United States during WINTER

Most intensive field studies investigating aerosols have been conducted in summer, and thus, wintertime aerosol sources and chemistry are comparatively poorly understood. An aerosol mass spectrometer was flown on the National Science Foundation/National Center for Atmospheric Research C‐130 during the Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) 2015 campaign in the northeast United States. The fraction of boundary layer submicron aerosol that was organic aerosol (OA) was about a factor of 2 smaller than during a 2011 summertime study in a similar region. However, the OA measured in WINTER was almost as oxidized as OA measured in several other studies in warmer months of the year. Fifty‐eight percent of the OA was oxygenated (secondary), and 42% was primary (POA). Biomass burning OA (likely from residential heating) was ubiquitous and accounted for 33% of the OA mass. Using nonvolatile POA, one of two default secondary OA (SOA) formulations in GEOS‐Chem (v10‐01) shows very large underpredictions of SOA and O/C (5×) and overprediction of POA (2×). We strongly recommend against using that formulation in future studies. Semivolatile POA, an alternative default in GEOS‐Chem, or a simplified parameterization (SIMPLE) were closer to the observations, although still with substantial differences. A case study of urban outflow from metropolitan New York City showed a consistent amount and normalized rate of added OA mass (due to SOA formation) compared to summer studies, although proceeding more slowly due to lower OH concentrations. A box model and SIMPLE perform similarly for WINTER as for Los Angeles, with an underprediction at ages \u3c6 hr, suggesting that fast chemistry might be missing from the models

Anthropogenic Control over Wintertime Oxidation of Atmospheric Pollutants

Anthropogenic air pollutants such as nitrogen oxides (NO(x) = NO + NO(2)), sulfur dioxide (SO(2)), and volatile organic compounds (VOC), among others, are emitted to the atmosphere throughout the year from energy production and use, transportation, and agriculture. These primary pollutants lead to the formation of secondary pollutants such as fine particulate matter (PM(2.5)) and ozone (O(3)) and perturbations to the abundance and lifetimes of short-lived greenhouse gases. Free radical oxidation reactions driven by solar radiation govern the atmospheric lifetimes and transformations of most primary pollutants and thus their spatial distributions. During winter in the mid and high latitudes, where a large fraction of atmospheric pollutants are emitted globally, such photochemical oxidation is significantly slower. Using observations from a highly instrumented aircraft, we show that multi-phase reactions between gas-phase NO(x) reservoirs and aerosol particles, as well as VOC emissions from anthropogenic activities, lead to a suite of atypical radical precursors dominating the oxidizing capacity in polluted winter air, and thus, the distribution and fate of primary pollutants on a regional to global scale

Ethnicity and incidence of Hodgkin lymphoma in Canadian population

<p>Abstract</p> <p>Background</p> <p>Research has shown that ethnicity is a significant predictor of Hodgkin lymphoma (HL). Variations in cancer incidence among ethnic groups in the same country can lead to important information in the search for etiological factors. Other risk factors important in the etiology of HL are medical history and exposure to pesticides. In this report we investigated the association between ethnicity and HL in the presence of medical history, and exposure to pesticides.</p> <p>Methods</p> <p>The data resulting from a matched population-based case-control study conducted in six provinces of Canada (Ontario, Quebec, Manitoba, Saskatchewan, Alberta, and British Columbia) was analyzed to determine whether or not there was any association between ethnicity and incidence of HL when adjusted for personal medical history and pesticide exposure. Information on ethnicity, personal medical history, and pesticide exposure was collected by questionnaires via mail on 316 men diagnosed with HL; and on 1506 controls. A conditional logistic regression was utilized and results were presented as odds ratios and 95% confidence intervals.</p> <p>Results</p> <p>In our study population, the distribution of ethnic groups was: 38.5% North American, 15% British, 8.4% Western European, 8.2% Eastern European, 1.7% Asian, 1.4% Scandinavian and 27% of other ethnic origin. Compared to North Americans (i) the risk of HL was greater among the Eastern European descendents (Odds Ratio (OR_adj): 1.82; 95% confidence interval (CI): 1.02, 3.25) and Western European (OR_adj: 1.62; 95% CI: 0.95–2.76) descent population (borderline significance at 5% level); and (ii) the risk of HL was lower in Asian descents. Diagnosis with measles (OR_adj: 0.72, 95% C.I.: 0.53–0.98) and/or positive history of allergy desensitization shots (OR_adj: 0.55, 95% C.I.: 0.30–0.99) were negatively associated with the incidence of HL, while diagnosis with acne (OR_adj: 2.12, 95% C.I.: 1.19–3.78), shingles (OR_adj: 2.41, 95% C.I.: 1.38–4.22) and positive family history of cancer (OR_adj: 1.93, 95% C.I.: 1.40–2.65) increased the risk of HL. Exposure to individual herbicide dichlorprop showed an increased risk of HL (OR_adj: 6.35, 95% C.I.: 1.56–25.92).</p> <p>Conclusion</p> <p>In Canada, compared to North Americans descendents, the risk of HL was significantly greater among the Eastern European and Western European descent population. Our results related to association between ethnicity and HL support the findings reported by other researchers. Our data showed that subjects who were diagnosed with measles or had allergy desensitization shots negatively associated with the incidence of HL; and other medical conditions, ever diagnosed with acne, and positive family history of cancer were positively associated with the incidence of HL.</p

Pesticides and health: A review of evidence on health effects, valuation of risks, and benefit‐cost analysis

In this paper, we provide reviews of recent scientific findings on health effects and preference valuation of health risks related to pesticides, and the role of benefit‐cost analysis in policies related to pesticides. Our reviews reveal that whereas the focus of the health literature has been on individuals with direct exposure to pesticides, e.g. farmers, the literature on preference elicitation has focused on those with indirect exposure, e.g. consumers. Our discussion of pesticides policies emphasizes the need to clarify the rationale for regulation and the role of risk perceptions in benefit‐cost analysis, and stress the importance of inter‐disciplinary research in this area

The ACOS CO_2 retrieval algorithm – Part 1: Description and validation against synthetic observations

This work describes the NASA Atmospheric CO_2 Observations from Space (ACOS) X_(CO_2) retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small

Limitations in representation of physical processes preven successful simulation of PM2.5 during KORUS-AQ

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea United States-Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. In the surface level, the model underestimates campaign average sulfate aerosol by −64 % but overestimates nitrate aerosol by 36 %. The largest underestimate in sulfate occurs during the pollution event in conditions of high relative humidity, where models typically struggle to generate the high concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a missing sink, which we implement here as a factor of five increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model’s inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66 % to 54 %. Locally-produced sulfate increased from 1 % to 46 % of locally-produced PM2.5, implying that local emissions controls would have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction which affects the aerosol liquid water abundance and chemistry driving sulfate-nitrate-ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate+nitrate+ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements

Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100&thinsp;000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25&thinsp;%, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20&thinsp;% over land and 40&thinsp;% over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.</p

Clustering of cancer among families of cases with Hodgkin Lymphoma (HL), Multiple Myeloma (MM), Non-Hodgkin's Lymphoma (NHL), Soft Tissue Sarcoma (STS) and control subjects

<p>Abstract</p> <p>Background</p> <p>A positive family history of chronic diseases including cancer can be used as an index of genetic and shared environmental influences. The tumours studied have several putative risk factors in common including occupational exposure to certain pesticides and a positive family history of cancer.</p> <p>Methods</p> <p>We conducted population-based studies of Hodgkin lymphoma (HL), Multiple Myeloma (MM), non-Hodgkin's Lymphoma (NHL), and Soft Tissue Sarcoma (STS) among male incident case and control subjects in six Canadian provinces. The postal questionnaire was used to collect personal demographic data, a medical history, a lifetime occupational history, smoking pattern, and the information on family history of cancer. The family history of cancer was restricted to first degree relatives and included relationship to the index subjects and the types of tumours diagnosed among relatives. The information was collected on 1528 cases (HL (n = 316), MM (n = 342), NHL (n = 513), STS (n = 357)) and 1506 age ± 2 years and province of residence matched control subjects. Conditional logistic regression analyses adjusted for the matching variables were conducted.</p> <p>Results</p> <p>We found that most families were cancer free, and a minority included two or more affected relatives. HL [(OR_adj(95% CI) <b>1.79 (1.33, 2.42)]</b>, MM <b>(1.38(1.07, 1.78))</b>, NHL <b>(1.43 (1.15, 1.77)</b>), and STS cases <b>(1.30(1.00, 1.68)) </b>had higher incidence of cancer if any first degree relative was affected with cancer compared to control families. Constructing mutually exclusive categories combining "family history of cancer" (yes, no) and "pesticide exposure ≥10 hours per year" (yes, no) indicated that a positive family history was important for HL <b>(2.25(1.61, 3.15))</b>, and for the combination of the two exposures increased risk for MM <b>(1.69(1.14,2.51))</b>. Also, a positive family history of cancer both with <b>(1.72 (1.21, 2.45)) </b>and without pesticide exposure <b>(1.43(1.12, 1.83)) </b>increased risk of NHL.</p> <p>Conclusion</p> <p>HL, MM, NHL, and STS cases had higher incidence of cancer if any first degree relative affected with cancer compared to control families. A positive family history of cancer and/or shared environmental exposure to agricultural chemicals play an important role in the development of cancer.</p