Human health is strongly affected by the concentration of fine particulate matter (PM_2.5). The need to forecast unhealthy conditions has driven the development of Chemical Transport Models such as Community Multi-Scale Air Quality (CMAQ). These models attempt to simulate the complex dynamics of chemical transport by combined meteorology, emission inventories (EI’s), and gas/particle chemistry and dynamics. Ultimately, the goal is to establish useful forecasts that could provide vulnerable members of the population with warnings. In the simplest utilization, any forecast should focus on next day pollution levels, and should be provided by the end of the business day (5 p.m. local). This paper explores the potential of different approaches in providing these forecasts. First, we assess the potential of CMAQ forecasts at the single grid cell level (12 km), and show that significant variability not encountered in the field measurements occurs. This observation motivates the exploration of other data driven approaches, in particular, a neural network (NN) approach. This approach makes use of meteorology and PM_2.5 observations as model predictors. We find that this approach generally results in a more accurate prediction of future pollution levels at the 12 km spatial resolution scale of CMAQ. Furthermore, we find that the NN is able to adjust to the sharp transitions encountered in pollution transported events, such as smoke plumes from forest fires, more accurately than CMAQ.

Continuous observation and quantitative retrieval of aerosol backscatter coefficients are important in the study of air quality and climate in metropolitan areas such as New York City. Ceilometers are ideal for this application, but aerosol backscatter coefficient retrievals from ceilometers are challenging and require proper calibration. In this study, we calibrate the ceilometer (Lufft CHM15k, 1064 nm) system constant with the molecular backscatter coefficient and evaluate the calibrated profiles with other independent methods, including the water-phase cloud method and comparison with the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) attenuated backscatter coefficient profile. Multiple-day calibration results show a stable system constant with a relative uncertainty of about 7%. We also evaluate the overlap correction for the CHM15k ceilometer (provided by Lufft) with a Vaisala CL-31 ceilometer, and the results show good consistency between two ceilometers’ range-corrected signal (RCS) profiles above 200 m. Next, we implement a forward iterative method to retrieve aerosol backscatter coefficients from continuous ceilometer measurements. In the retrieval, the lidar ratio is constrained by the co-located NASA AERONET radiometer aerosol optical depth (AOD) retrieval and agrees with the AERONET lidar-ratio products, derived from aerosol microphysical parameters. The aerosol backscatter coefficient retrievals are validated with co-located elastic-Raman lidar retrievals and indicate a good correlation ($R^2\ge 0.95$) in the planetary boundary layer (PBL). Furthermore, a case study shows that the ceilometer retrieved aerosol extinction coefficient profiles can be used to estimate the AOD of the PBL and the aloft plumes. Finally, simulations of the uncertainty of aerosol backscatter coefficient retrieval show that a calibration error of 10% results in 10–20% of relative error in the aerosol backscatter coefficient retrievals, while relative error caused by a lidar-ratio error of 10% is less than 4% in the PBL.

Excitation dynamics in ${\mathrm{Er}}^{\text{3+}}–{\mathrm{Yb}}^{\text{3+}}$ co-doped phosphate glasses were examined as a function of both erbium and ytterbium concentration by monitoring fluorescence on the 1.5 μm ${}^4{I}_{\text{13/2}}{\to }^4{I}_{\text{15/2}}$ transition of ${\mathrm{Er}}^{\text{3+}}$ following pulsed laser excitation of the system at 977 nm under room temperature. For the range of donor $({\mathrm{Yb}}^{\text{3+}})$ and acceptor $({\mathrm{Er}}^{\text{3+}})$ concentration considered, both nonexponential and exponential relaxation dynamics were observed. Nonexponential acceptor fluorescence is characteristic of noninteracting donor $({\mathrm{Yb}}^{\text{3+}})$ regime, while the exponential response is consistent with interacting donors. For the latter case, the linear acceptor dependence for the ${\mathrm{Yb}}^{\text{3+}}–{\mathrm{Er}}^{\text{3+}}$ transfer rate K implies energy super migration among donor ions.

The possible interaction and modification of cloud properties due to aerosols is one of the most poorly understood mechanisms within climate studies, resulting in the most significant uncertainty as regards radiation budgeting. In this study, we explore direct ground based remote sensing methods to assess the Aerosol-Cloud Indirect Effect directly, as space-borne retrievals are not directly suitable for simultaneous aerosol/cloud retrievals. To illustrate some of these difficulties, a statistical assessment of existing multispectral imagers on geostationary (e.g., GOES)/Moderate Resolution Imaging Spectroradiometer (MODIS) satellite retrievals of the Cloud Droplet Effective Radius (R_eff) showed significant biases especially at larger solar zenith angles, further motivating the use of ground based remote sensing approaches. In particular, we discuss the potential of using a combined Microwave Radiometer (MWR)—Multi-Filter Rotating Shadowband Radiometer (MFRSR) system for real-time monitoring of Cloud Optical Depth (COD) and Cloud Droplet Effective Radius (R_eff), which are combined with aerosol vertical properties from an aerosol lidar. An iterative approach combining the simultaneous observations from MFRSR and MWR are used to retrieve the COD and R_eff for thick cloud cases and are extensively validated using the DoE Southern Great Plains (SGP) retrievals as well as regression based parameterized model retrievals. In addition, we account for uncertainties in background aerosol, surface albedo and the combined measurement uncertainties from the MWR and MFRSR in order to provide realistic uncertainty estimates, which is found to be ~10% for the parameter range of interest in Aerosol-Cloud Interactions. Finally, we analyze a particular case of possible aerosol-cloud interaction described in the literature at the SGP site and demonstrate that aerosol properties obtained at the surface can lead to inconclusive results in comparison to lidar-derived aerosol properties near the cloud base.

Health risks connected with fine particulate matter (PM_2.5) pollutants are well documented; increased risks of asthma, heart attack and heart failure are a few of the effects associated with PM_2.5. Accurately forecasting PM_2.5 is crucial for state agencies directed to devise State Implementation Plans (SIPS) to deal with National Ambient Air Quality Standards (NAAQS) exceedances. In previous work, we explored the application of multi-temporal data-driven neural networks (NNs) to forecasting PM_2.5. Our work showed that under different input conditions, the NN approach achieves higher forecasting scores for local (12 km) resolution when compared to the other Chemical Transport Model forecast models, such as the Community Multi-Scale Air Quality system (CMAQ). Critical to our approach was the inclusion of prior PM_2.5 concentrations, retrieved from ground monitoring stations, as part of the input dataset for the NN. The NN approach can provide high-level forecasting accuracy; however, because of the dependency on ground monitoring stations, the forecast coverage is sparse. Here, we extend our previous station-specific efforts by forecasting hourly PM_2.5 values that are spatially continuous through the use of a deep neural network (DNN). The DNN approach combines spatial Kriging with additional local source variables to interpolate the measured PM_2.5 concentrations across non-station locations. These interpolated PM_2.5 values are used as inputs in the original forecasting NN. Cross-validation testing, using all New York State AirNow PM_2.5 stations, showed that this forecast approach achieves accurate results, with a regression coefficient (R²) of 0.59, and a root mean square error (RMSE) of 2.22$\frac{\mu g}{m^3}$. Additionally, herein we demonstrate the usefulness of this approach on specific temporal events where significant dynamics of PM_2.5 were observed; particularly, we show that even bias-corrected CMAQ forecasts do not track these transients and our NN method.