PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model

Airborne and ground-based Pandora spectrometer NOspan classCombining double low line"inline-formula"2/span column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NOspan classCombining double low line"inline-formula"2/span Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NOspan classCombining double low line"inline-formula"2/span. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (span classCombining double low line"inline-formula"ir/i2Combining double low line/spanthinsp;0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250thinsp;mthinsp;span classCombining double low line"inline-formula"×/spanthinsp;250thinsp;m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (span classCombining double low line"inline-formula"ir/i2Combining double low line0.96/span) than Pandora measurements are with TROPOMI (span classCombining double low line"inline-formula"ir/i2Combining double low line0.84/span). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5span classCombining double low line"inline-formula"g /span) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4thinsp;%-11thinsp;%. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19thinsp;%-33thinsp;% during the LISTOS timeframe of June-September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing thesespan idCombining double low line"page6114"/ profiles with those from a 12thinsp;km North American Model-Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12thinsp;%-14thinsp;% increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7thinsp;%-19thinsp;% low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets./.

/pdf/evaluating-sentinel-5p-tropomi-tropospheric-no2-column-3hyck9e43y.pdf

Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound

This study aimed to explore the pattern of accumulation of some of main heavy metals in blood and various organs of rats after exposed to the atmospheric fine particulate matter (PM2.5). Rats were randomly divided into control and three treatment groups (tracheal perfusion with 10 mg/kg, 20 mg/kg and 40 mg/kg of PM2.5 suspension liquid, respectively). Whole blood and the lung, liver, kidney, and cerebral cortex were harvested after rats were treated and sacrificed. The used heavy metals were detected using inductively coupled plasma-mass spectrometry (ICP-MS) instrument. As results, Lead was increased in the liver, lung and cerebral cortex and the level of manganese was significantly elevated in the liver and cerebral cortex in PM2.5 treated rats. Besides, arsenic was prominently enriched both in cerebral cortex and in blood, and so did the aluminum in the cerebral cortex and the copper in the liver. However, cadmium, chromium and nickel have shown no difference between the control group and the three PM2.5 treated groups. Following the exposure of PM2.5, different heavy metals are preferentially accumulated in different body tissues.

/pdf/the-preferential-accumulation-of-heavy-metals-in-different-am7o44onyj.pdf

The preferential accumulation of heavy metals in different tissues following frequent respiratory exposure to PM2.5 in rats

Long-term NOx trends over large cities in the United States during the great recession: Comparison of satellite retrievals, ground observations, and emission inventories

Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which ...

https://www.tandfonline.com/doi/pdf/10.1080/10962247.2016.1158214

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

The US National Air Quality Forecast Capability (NAQFC), developed by the National Oceanic and Atmospheric Administration (NOAA) in partnership with the Environmental Protection Agency (EPA), currently provides next-day operational predictions for ground level ozone and smoke for 50 US states. Ozone predictions are produced with the Community Multiscale Air Quality (CMAQ) model driven by NOAA’s operational North American Mesoscale weather forecast Model (NAM); routine verification is conducted with monitoring data compiled by the EPA. Smoke prediction relies on satellite detections of smoke sources, US Forest Service emission estimates, with transport and dispersion simulated by the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model driven by NAM; routine verification is conducted with satellite observations of smoke. Quantitative predictions of fine particulate matter (PM2.5) are in development. Inventory based simulations using CMAQ with aerosol modules show seasonal biases: overestimating in wintertime and underestimating in summertime. Current testing focuses on including intermittent aerosol sources directly emitted by wildfires and dust storms within the forecast domain; longer-range transport of dust is incorporated through lateral boundary conditions. For example, simulations of trans-Atlantic transport of Saharan dust, injected into the prediction domain, contribute enhanced surface PM2.5 concentrations in the southern US, as observed in surface monitoring. Simulated PM2.5 concentrations are being evaluated with speciated observations in order to improve seasonal biases in predictions. Research on assimilating PM2.5 surface observations shows potential to improve predictions. Furthermore, analysis of discrepancies between observations and model predictions that are produced during assimilation can provide insight on impacts of proposed improvements to PM2.5 predictions.

US National Air Quality Forecast Capability: Expanding Coverage to Include Particulate Matter

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US National Air Quality Forecast Capability: Expanding Coverage to Include Particulate Matter