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TRACER-MAP operated Jul - Aug 2022 as an ASR and ARM project. TRACER-MAP measured and mapped aerosol, volatile organic compounds, trace gas and select meteorological observations across the Houston domain during TRACER. The dataset has been corrected for truncation error using Bond et al, 2009.
The dataset contains aerosol light absorption and scattering coefficients measured by three single-wavelength Integrated Photoacoustic Nephelometers (IPN). The absorption coefficients were measured at 405, 721, and 1047 nm, while the scattering coefficients were measured at 405 and 721 nm. The single scattering albedo (SSA) at 405 and 721 nm were also calculated from the IPN measurements. The three IPNs shared a common inlet without impactor/cyclone in July; in August, a PM2.5 cyclone inlet was installed to reduce measurement noise and improve data quality. The 721-nm IPN experienced laser shutdown issues, causing missing of a large fraction of the 721-nm measurements, especially in July. As described before, the 721-nm laser issue was less frequent in August after the cyclone installation, improving the data completeness and quality. The IPN raw data were noisy, making identifying patterns and trends difficult; therefore, the hourly average data are recommended over the raw data. The hourly average data were cross-checked with the filter-based TAP and PSAP measurements, which showed that their patterns, trends, and peaks matched. However, due to the differences in working mechanisms and principles of the filter-based and photoacoustic particle-phase instruments, the IPN measurements are lower than the filter-based measurements despite that they have consistent patterns, trends, and peaks.
The data set of aerosol light absorption coefficients were collected by two tricolor absorption photometers (TAP; Brechtel Model 2901) in July and August, 2022 during the TRACER field campaign at the AMF1 site in La Porte, Texas. One of the TAPs at the La Porte site was an RGB model measuring aerosol light absorption at 652, 528, 467 nm, and the other was an UV model measuring absorption at the same red and green wavelengths but at an additional UV wavelength at 365 nm. Since TAP is a filter-based optical instrument using 47-mm glass fiber filters, the output absorption coefficients are corrected for filter loading and scattering effects based on a correction scheme developed in "Comment on ‘Calibration and Intercomparison of Filter-Based Measurements of Visible Light Absorption by Aerosols’" by John A. Ogren 2010. This uploaded dataset contains both the Ogren corrected absorption coefficients (column header with “ Ogren”) and the raw absorption coefficients without the correction scheme (column header with “_ Raw”). During the field campaign, the two TAPs operated side by side with a shared inlet at a flow rate of 2 lpm. In July, no inlet impactors/cyclones were installed; however, beginning in August, a PM2.5 cyclone inlet was installed.
Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.
Modeling of long-term trends of aerosols and their properties is important for constraining aerosol-climate forcing, and for characterizing changes in particulate matter pollution speciation and exposure. Here we study global and regional long-term trends in surface fine particulate matter (PM ) and aerosol optical properties for 60 years from 1958 to 2018, using simulations performed with the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM), and evaluate the model hindcast with observations for the last three decades. Comparing the modeled aerosols with a diverse set of observations helps interpret observed and simulated trends, and serves as a benchmark for future GEOSCCM developments and input datasets improvements. We first characterize modeled global and regional temporal changes in surface PM and its components, aerosol optical depth (AOD) and single scattering albedo (SSA) and we interpret their link with emissions drivers. We then compare modeled surface PM with ground-based observations from monitoring networks and with global reconstructed PM datasets from observations-model data fusion. Total AOD is compared with long-term satellite measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) and measurements from the ground-based Aerosol Robotic Network (AERONET). Additional aerosol optical properties, such as absorption and scattering coefficients, are evaluated with ground-based observations from the Global Atmosphere Watch (GAW) records.