U.S. National PM<sub>2.5</sub>Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

Solomon, Paul A.; Crumpler, Dennis; Flanagan, James; Jayanty, R. K. M.; Rickman, Ed; McDade, Charles E.

doi:10.1080/10962247.2014.956904

Cited by 220 publications

(183 citation statements)

References 65 publications

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“…A common approach to account for the sampling artifacts of nitrate is to use a HNO 3 denuder in combination with a TefloneNylon filter pack. This technique has been implemented in the PM 2.5 chemical speciation monitoring networks such as the PM 2.5 National Chemical Speciation Network (CSN) and the IMPROVE network established by the U.S. Environmental Protection Agency (EPA) (Solomon et al, 2014), and the National Air Pollution Surveillance (NAPS) network in Canada (DabekZlotorzynska et al, 2011). The HNO 3 denuders usually use sodium carbonate (Na 2 CO 3 ) or magnesium oxide (MgO) as the sorbent.…”

Section: Campaigns Sass Configurationmentioning

confidence: 99%

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Cheng

2015

Environmental Pollution

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Section: Campaigns Sass Configurationmentioning

confidence: 99%

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Cheng

2015

Environmental Pollution

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“…Quartz filters were analyzed using the Interagency Monitoring of Protected Visual Environments-A (IMPROVE_A) thermal-optical reflectance method (Chow et al, 2007b to determine EC and OC fractions in the sampled particulates. The minimum detection limits of the thermal-optical reflectance analysis are about 9 µg for OC and 1 µg for EC (Solomon et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Aerosol emissions factors from traditional biomass cookstoves in India: insights from field measurements

et al. 2017

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Abstract. Residential solid biomass cookstoves are important sources of aerosol emissions in India. Cookstove emissions rates are largely based on laboratory experiments conducted using the standard water-boiling test, but real-world emissions are often higher owing to different stove designs, fuels, and cooking methods. Constraining mass emissions factors (EFs) for prevalent cookstoves is important because they serve as inputs to bottom-up emissions inventories used to evaluate health and climate impacts. Real-world EFs were measured during winter 2015 for a traditional cookstove (chulha) burning fuel wood, agricultural residue, and dung from different regions of India. Average (±95 % confidence interval) EFs for fuel wood, agricultural residue, and dung were (1) PM 2.5 mass: 10.5 (7.7-13.4) g kg −1 , 11.1 (7.7-15.5) g kg −1 , and 22.6 (14.9-32.9) g kg −1 , respectively; (2) elemental carbon (EC): 0.9 (0.6-1.4) g kg −1 , 1.6 (0.6-3.0) g kg −1 , and 1.0 (0.4-2.0) g kg −1 , respectively; and (3) organic carbon (OC): 4.9 (3.2-7.1) g kg −1 , 7.0 (3.5-12.5) g kg −1 , and 12.9 (4.2-15.01) g kg −1 , respectively. The mean (±95 % confidence interval) OC / EC mass ratios were 6.5 (4.5-9.1), 7.6 (4.4-12.2), and 12.7 (6.5-23.3), respectively, with OC and EC quantified by the IMPROVE_A thermal-optical reflectance protocol. These real-world EFs are higher than those from previous laboratory-based measurements. Combustion conditions have larger effects on EFs than the fuel types. We also report the carbon mass fractions of our aerosol samples determined using the thermal-optical reflectance method. The mass fraction profiles are consistent between the three fuel categories but markedly different from those reported in past literature -including the source profiles for wood stove PM 2.5 emissions developed as inputs to receptor modeling studies conducted by the Central Pollution Control Board of India. Thermally stable OC (OC3 in the IMPROVE_A protocol) contributed nearly 50 % of the total carbon mass for emissions from all fuels.

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“…Surface PM 2.5 speciation and PM 10 (particulate matter with a diameter ≤ 10 µm) data are routinely collected by two national chemical speciation monitoring networks: the Interagency Monitoring of Protected Visual Environments (IMPROVE) and the PM 2.5 National Chemical Speciation Network (CSN) operated by the Environmental Protection Agency (EPA) (Hand et al, 2011;Solomon et al, 2014). IMPROVE has collected 24 h aerosol speciation every third day at mostly rural sites since 1988.…”

Section: Surface Mass Concentrationmentioning

confidence: 99%

WRF-Chem simulation of aerosol seasonal variability in the San Joaquin Valley

Калашникова

et al. 2017

Atmos. Chem. Phys.

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Abstract. WRF-Chem simulations of aerosol seasonal variability in the San Joaquin Valley (SJV), California, are evaluated by satellite and in situ observations. Results show that the WRF-Chem model successfully captures the distribution and magnitude of and variation in SJV aerosols during the cold season. However, aerosols are not well represented in the warm season. Aerosol simulations in urban areas during the cold season are sensitive to model horizontal resolution, with better simulations at 4 km resolution than at 20 km resolution, mainly due to inhomogeneous distribution of anthropogenic emissions and precipitation that is represented better in the 4 km simulation. In rural areas, the model sensitivity to grid size is rather small. Our observational analysis reveals that dust is a primary contributor to aerosols in the SJV, especially during the warm season. Aerosol simulations in the warm season are sensitive to the parameterization of dust emission in WRF-Chem. The GOCART (Goddard Global Ozone Chemistry Aerosol Radiation and Transport) dust scheme produces very little dust in the SJV, while the DUSTRAN (DUST TRANsport model) scheme overestimates dust emission. Vertical mixing of aerosols is not adequately represented in the model based on CALIPSO (CloudAerosol Lidar and Infrared pathfinder Satellite Observation) aerosol extinction profiles. Improved representation of dust emission and vertical mixing in the boundary layer is needed for better simulations of aerosols during the warm season in the SJV.

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U.S. National PM_2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

Cited by 220 publications

References 65 publications

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Aerosol emissions factors from traditional biomass cookstoves in India: insights from field measurements

WRF-Chem simulation of aerosol seasonal variability in the San Joaquin Valley

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U.S. National PM2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

Cited by 220 publications

References 65 publications

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Aerosol emissions factors from traditional biomass cookstoves in India: insights from field measurements

WRF-Chem simulation of aerosol seasonal variability in the San Joaquin Valley

Contact Info

Product

Resources

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U.S. National PM_2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks