Identification of the roles of urban plume and local chemical production in ozone episodes observed in Long Island Sound during LISTOS 2018: Implications for ozone control strategies.

Long Island Sound (LIS) frequently experiences ozone (O3) exceedance events that surpass national ambient air quality standards (NAAQS) due to complex driving factors. The underlying mechanisms governing summertime O3 pollution are investigated through collaborative observations from lidar remote sensing and ground samplers during the 2018 LIS Tropospheric O3 Study (LISTOS). Regional transport and local chemical reactions are identified as the two key driving factors behind the observed O3 episodes in LIS. An enhanced laminar structure is observed in the O3 vertical structure in the atmospheric boundary layer (i.e., 0-2 km layer) for the case dominated by regional transport. An O3 formation regime shift is found in ozone-precursor sensitivity (OPS) for the O3 exceedance event dominated by regional transport with NOx-enriched air mass transport from the New York City (NYC) urban area to LIS. Furthermore, the Integrated Process Rate (IPR) analysis demonstrates that transport from the NYC urban area contributed 40% and 27.1% of surface O3 enhancement to the cases dominated by regional transport and local production, respectively. This study provides scientific evidence to uncovers two key processes that govern summertime O3 pollution over LIS and can help to improve emission control strategies to meet the attainment standards for ambient O3 levels over LIS and other similar coastal areas.

Volatile chemical product emissions enhance ozone and modulate urban chemistry.

Decades of air quality improvements have substantially reduced the motor vehicle emissions of volatile organic compounds (VOCs). Today, volatile chemical products (VCPs) are responsible for half of the petrochemical VOCs emitted in major urban areas. We show that VCP emissions are ubiquitous in US and European cities and scale with population density. We report significant VCP emissions for New York City (NYC), including a monoterpene flux of 14.7 to 24.4 kg ⋅ d-1 ⋅ km-2 from fragranced VCPs and other anthropogenic sources, which is comparable to that of a summertime forest. Photochemical modeling of an extreme heat event, with ozone well in excess of US standards, illustrates the significant impact of VCPs on air quality. In the most populated regions of NYC, ozone was sensitive to anthropogenic VOCs (AVOCs), even in the presence of biogenic sources. Within this VOC-sensitive regime, AVOCs contributed upwards of ∼20 ppb to maximum 8-h average ozone. VCPs accounted for more than 50% of this total AVOC contribution. Emissions from fragranced VCPs, including personal care and cleaning products, account for at least 50% of the ozone attributed to VCPs. We show that model simulations of ozone depend foremost on the magnitude of VCP emissions and that the addition of oxygenated VCP chemistry impacts simulations of key atmospheric oxidation products. NYC is a case study for developed megacities, and the impacts of VCPs on local ozone are likely similar for other major urban regions across North America or Europe.

Synergistic aircraft and ground observations of transported wildfire smoke and its impact on air quality in New York City during the 2018 LISTOS campaign.

Air pollution associated with wildfire smoke transport during the summer can significantly affect ozone (O3) and particulate matter (PM) concentrations, even in heavily populated areas like New York City (NYC). Here, we use observations from aircraft, ground-based lidar, in-situ analyzers and satellite to study and assess wildfire smoke transport, vertical distribution, optical properties, and potential impact on air quality in the NYC urban and coastal areas during the summer 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We investigate an episode of dense smoke transported and mixed into the planetary boundary layer (PBL) on August 15-17, 2018. The horizontal advection of the smoke is shown to be characterized with the prevailing northwest winds in the PBL (velocity > 10 m/s) based on Doppler wind lidar measurements. The wildfire sources and smoke transport paths from the northwest US/Canada to northeast US are identified from the NOAA hazard mapping system (HMS) fires and smoke product and NOAA-HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory analysis. The smoke particles are distinguished from the urban aerosols by showing larger lidar-ratio (70-sr at 532-nm) and smaller depolarization ratio (0.02) at 1064-nm using the NASA High Altitude Lidar Observatory (HALO) airborne high-spectral resolution lidar (HSRL) measurements. The extinction-related angstrom exponents in the near-infrared (IR at 1020-1640 nm) and Ultraviolet (UV at 340-440 nm) from NASA-Aerosol Robotic Network (AERONET) product show a reverse variation trend along the smoke loadings, and their absolute differences indicate strong correlation with the smoke-Aerosol Optical Depth (AOD) (R > 0.94). We show that the aloft smoke plumes can contribute as much as 60-70% to the column AOD and that concurrent high-loadings of O3, carbon monoxide (CO), and black carbon (BC) were found in the elevated smoke layers from the University of Maryland (UMD) aircraft in-situ observations. Meanwhile, the surface PM2.5 (PM with diameter ≤ 2.5 µm), organic carbon (OC) and CO measurements show coincident and sharp increase (e.g., PM2.5 from 5 µg/m3 before the plume intrusion to ~30 µg/m3) with the onset of the plume intrusions into the PBL along with hourly O3 exceedances in the NYC region. We further evaluate the NOAA-National Air Quality Forecasting Capability (NAQFC) model PBL-height, PM2.5, and O3 with the observations and demonstrate good consistency near the ground during the convective PBL period, but significant bias at other times. The aloft smoke layers are sometimes missed by the model.

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models.

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

A Hardware Implemented Autocorrelation Technique for Estimating Power Spectral Density for Processing Signals from a Doppler Wind Lidar System.

A signal processing technique utilizing autocorrelation of backscattered signals was designed and implemented in a 1.5 µm all-fiber wind sensing Coherent Doppler Lidar (CDL) system to preprocess atmospheric signals. The signal processing algorithm's design and implementation are presented. The system employs a 20 kHz pulse repetition frequency (PRF) transmitter and samples the return signals at 400 MHz. The logic design of the autocorrelation algorithm was developed and programmed into a field programmable gate array (FPGA) located on a data acquisition board. The design generates and accumulates real time correlograms representing average autocorrelations of the Doppler shifted echo from a series of adjustable range gates. Accumulated correlograms are streamed to a host computer for subsequent processing to yield a line of sight wind velocity. Wind velocity estimates can be obtained under nominal aerosol loading and nominal atmospheric turbulence conditions for ranges up to 3 km.

Application of a broadly tunable SG-DBR QCL for multi-species trace gas spectroscopy.

Feasibility of using a mid-Infrared tunable sampled-grating distributed Bragg reflectors quantum cascade laser for high resolution multicomponent trace gas spectroscopy is demonstrated. By controlling the driving currents to the front and back sections of the laser, we were able to tune a pulsed 4.55 µm laser over a frequency range a of 30 cm(-1) with high resolution, accuracy and repeatability. The laser was applied to absorption spectroscopy of ambient and reduced pressure (150 Torr) air in a 205 meters multi-pass Herriott cell, and by using standard LSQ fitting to a spectral database of these trace gases (HITRAN), the concentrations of nitrous oxide, carbon monoxide, and water vapor were retrieved.

Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS.

Retrieving the inherent optical properties of water from remote sensing multispectral reflectance measurements is difficult due to both the complex nature of the forward modeling and the inherent nonlinearity of the inverse problem. In such cases, neural network (NN) techniques have a long history in inverting complex nonlinear systems. The process we adopt utilizes two NNs in parallel. The first NN is used to relate the remote sensing reflectance at available MODIS-visible wavelengths (except the 678 nm fluorescence channel) to the absorption and backscatter coefficients at 442 nm (peak of chlorophyll absorption). The second NN separates algal and nonalgal absorption components, outputting the ratio of algal-to-nonalgal absorption. The resulting synthetically trained algorithm is tested using both the NASA Bio-Optical Marine Algorithm Data Set (NOMAD), as well as our own field datasets from the Chesapeake Bay and Long Island Sound, New York. Very good agreement is obtained, with R² values of 93.75%, 90.67%, and 86.43% for the total, algal, and nonalgal absorption, respectively, for the NOMAD. For our field data, which cover absorbing waters up to about 6 m⁻¹, R² is 91.87% for the total measured absorption.

Calibration of the 1064 nm lidar channel using water phase and cirrus clouds.

Calibration is essential to derive aerosol backscatter coefficients from elastic scattering lidar. Unlike the visible UV wavelengths where calibration is based on a molecular reference, calibration of the 1064 nm lidar channel requires other approaches, which depend on various assumptions. In this paper, we analyze two independent calibration methods which use (i) low-altitude water phase clouds and (ii) high cirrus clouds. In particular, we show that to achieve optimal performance, aerosol attenuation below the cloud base and cloud multiple scattering must be accounted for. When all important processes are considered, we find that these two independent methods can provide a consistent calibration constant with relative differences less than 15%. We apply these calibration techniques to demonstrate the stability of our lidar on a monthly scale, along with a natural reduction of the lidar efficiency on an annual scale. Furthermore, our calibration procedure allows us to derive consistent aerosol backscatter coefficients and angstrom coefficient profiles (532-1064 nm) along with column extinction-to-backscatter ratios which are in good agreement with sky radiometer inversions.

Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery.

Karenia brevis (K. brevis) blooms are of great interest and have been commonly reported throughout the Gulf of Mexico. In this study we propose a detection technique for blooms with low backscatter characteristics, which we name the Red Band Difference (RBD) technique, coupled with a selective K. brevis bloom classification technique, which we name the K. brevis Bloom Index (KBBI). These techniques take advantage of the relatively high solar induced chlorophyll fluorescence and low backscattering of K. brevis blooms. The techniques are applied to the detection and classification of K. brevis blooms from Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color measurements off the Gulf of Mexico. To assess the efficacy of the techniques for detection and classification, simulations, including chlorophyll fluorescence (assuming 0.75% quantum yield) based on K. brevis blooms and non-K. brevis blooms conditions were performed. These show that effective bloom detection from satellite measurements requires a threshold of RBD>0.15W/m(2)/microm/sr, corresponding to about 5mg/m(3) of chlorophyll. Blooms can be detected at lower concentration by lowering the RBD threshold but false positives may increase. The classification technique is found most effective for thresholds of RBD>0.15W/m(2)/microm/sr and KBBI>0.3*RBD. The techniques were applied and shown to be effective for well documented blooms of K. brevis in the Gulf of Mexico and compared to other detection techniques, including FLH approaches. Impacts of different atmospheric corrections on results were also examined.

Polarized light in coastal waters: hyperspectral and multiangular analysis.

Measurements of the underwater polarized light field were performed at different stations, atmospheric conditions and water compositions using a newly developed hyperspectral and multiangular polarimeter during a recent cruise in the coastal areas of New York Harbor - Sandy Hook, NJ region (USA). Results are presented for waters with chlorophyll concentrations 1.3-4.8 microg/l and minerals concentrations 2.0- 3.9 mg/l. Angular and spectral variations of the degree of polarization are found to be consistent with theory. Maximum values of the degree of polarization do not exceed 0.4 and the position of the maximum is close to 100 masculine scattering angle. Normalized radiances and degrees of polarization are compared with simulated ones obtained with a Monte Carlo radiative transfer code for the atmosphere-ocean system and show satisfactory agreement.

Low and optically thin cloud measurements using a Raman-Mie lidar.

We analyze the potential of measuring low-altitude optically thin clouds with a Raman-elastic lidar in the daytime. Optical depths of low clouds are derived by two separate methods from nitrogen Raman and elastic-scattering returns. By correcting for aerosol influences with the combined Raman-elastic returns, Mie retrievals of low-cloud optical depth can be dramatically improved and show good agreement with the direct Raman retrievals. Furthermore, a lidar ratio profile is mapped out and shown to be consistent with realistic water phase cloud models. The variability of lidar ratios allows us to explore the distribution of small droplets near the cloud perimeter.

Improving atmospheric correction for highly productive coastal waters using the short wave infrared retrieval algorithm with water-leaving reflectance constraints at 412 nm.

The recently developed short wave infrared (SWIR) atmospheric correction algorithm for ocean color retrieval uses long wavelength channels to retrieve atmospheric parameters to avoid bright pixel contamination. However, this retrieval is highly sensitive to errors in the aerosol model, which is magnified by the higher variability of aerosols observed over urban coastal areas. While adding extra regional aerosol models into the retrieval lookup tables would tend to increase retrieval error since these models are hard to distinguish in the IR, we explore the possibility that for highly productive waters with high colored dissolved organic matter, an estimate of the 412 nm channel water-leaving reflectance can be used to constrain the aerosol model retrieval and improve the water-leaving reflectance retrieval. Simulations show that this constraint is particularly useful where aerosol diversity is significant. To assess this algorithm we compare our retrievals with the operational SeaWiFS Data Analysis System (SeaDAS) SWIR and near infrared retrievals using in situ validation data in the Chesapeake Bay and show that, especially for absorbing aerosols, significant improvement is obtained. Further insight is also obtained by the intercomparison of retrieved remote sensing reflectance images at 443 and 551 nm, which demonstrates the removal of anomalous artifacts in the operational SeaDAS retrieval.

Bayesian assessment of uncertainty in aerosol size distributions and index of refraction retrieved from multiwavelength lidar measurements.

We investigate the assessment of uncertainty in the inference of aerosol size distributions from backscatter and extinction measurements that can be obtained from a modern elastic/Raman lidar system with a Nd:YAG laser transmitter. To calculate the uncertainty, an analytic formula for the correlated probability density function (PDF) describing the error for an optical coefficient ratio is derived based on a normally distributed fractional error in the optical coefficients. Assuming a monomodal lognormal particle size distribution of spherical, homogeneous particles with a known index of refraction, we compare the assessment of uncertainty using a more conventional forward Monte Carlo method with that obtained from a Bayesian posterior PDF assuming a uniform prior PDF and show that substantial differences between the two methods exist. In addition, we use the posterior PDF formalism, which was extended to include an unknown refractive index, to find credible sets for a variety of optical measurement scenarios. We find the uncertainty is greatly reduced with the addition of suitable extinction measurements in contrast to the inclusion of extra backscatter coefficients, which we show to have a minimal effect and strengthens similar observations based on numerical regularization methods.

Particle size and refractive index retrieval from the backscattering spectrum of white light using the Twomey iterative method: simulation and experiment.

The Twomey iterative method has been applied to the retrieval of hydrosol microphysical properties. In particular, we focused on the retrieval of single and multimode particle size distributions from both simulated and experimental backscattering spectra in the 400-800 nm wavelength range. Assuming a known refractive index, both single-mode and multimode distributions were successfully retrieved through the introduction of an initial distribution biased toward larger particles. The simulation results were experimentally verified with standard polystyrene particles suspended in water within the diameter range of 0.2-2 microm for both narrow and broad monomodal distributions as well as more complicated multimode distributions. Finally, the technique was extended to the retrieval of an unknown refractive index.

Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations.

The impact and potential of a polarization-selection technique to reduce the sky background signal for linearly polarized monostatic elastic backscatter lidar measurements are examined. Taking advantage of naturally occurring polarization properties in scattered skylight, we devised a polarization-discrimination technique in which both the lidar transmitter and the receiver track and minimize detected sky background noise while maintaining maximum lidar signal throughput. Lidar elastic backscatter measurements, carried out continuously during daylight hours at 532 nm, show as much as a factor of square root 10 improvement in the signal-to-noise ratio (SNR) over conventional unpolarized schemes. For vertically pointing lidars, the largest improvements are limited to the early morning and late afternoon hours, while for lidars scanning azimuthally and in elevation at angles other than vertical, significant improvements are achievable over more extended time periods with the specific times and improvement factors depending on the specific angle between the lidar and the solar axes. The resulting diurnal variations in SNR improvement sometimes show an asymmetry with the solar angle that analysis indicates can be attributed to changes in observed relative humidity that modifies the underlying aerosol microphysics and observed optical depth.

Retrieval of chlorophyll fluorescence from reflectance spectra through polarization discrimination: modeling and experiments.

The polarization discrimination technique we recently developed, shows that it is possible to separate the elastic scattering and the chlorophyll fluorescence signal from the water-leaving radiance by making use of the fact that the elastically scattered components are partially polarized, while the fluorescence signal is unpolarized. The technique has been shown to be applicable to a wide range of water conditions. We present an extension of experimental and analytical results, which serve to define the scope of this technique and its range of applicability. A new analysis, based on vector radiative transfer computations, and on laboratory and field measurements on eastern Long Island and in the Chesapeake Bay, shows that the technique is generally effective for both open ocean and coastal waters, but that it is limited if the ocean bottom albedo and/or multiple scattering due to very high mineral particle concentrations result in depolarizing the water-leaving radiance. In addition, we show that in contrast with the polarization-based retrieval, the traditional method of extracting fluorescence height using the baseline method can give significant errors, particularly for coastal waters where it strongly overestimates the fluorescence values.

Extension of the graphical technique for estimation of particle size distribution parameters for the consistent intercomparison of diverse sets of multiwavelength lidar derived optical coefficients.

In applying the graphical technique to the estimation of the particle size distribution (PSD) parameters, determination of proper bounds surrounding the solution space for a particular confidence level is essential to the consistent intercomparison of diverse multiwavelength lidar optical data sets. The graphical technique utilizes ratios of backscatter and/or extinction coefficients, and it is shown that if the correlation between ratios is not taken into account in calculating the error bounds, the solution space will be overestimated, resulting in relatively larger discrepancies for a larger number of optical coefficients. A method for correcting the bounds, to account for the correlation is developed for various numbers of wavelengths. These improved bounds are then applied, for the case of a monomodal lognormal PSD, with an assumed refractive index, to assess the role additional Raman extinction channels play in improving retrieval capability of a typical three-channel backscatter lidar (1064, 532, and 355 nm) under varying noise levels. Applying the same formalism to underlying bimodal distributions of coarse and fine particles can result in false monomodal solutions. However, when both Raman optical extinction channels are available, no solution is obtained. This can potentially serve as a quick and simple method, prior to a more complex regularization analysis, to differentiate between cases in which the fine mode is dominant versus the cases in which the contribution from the coarse mode is significant.