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 (O) exceedance events that surpass national ambient air quality standards (NAAQS) due to complex driving factors. The underlying mechanisms governing summertime O pollution are investigated through collaborative observations from lidar remote sensing and ground samplers during the 2018 LIS Tropospheric O Study (LISTOS). Regional transport and local chemical reactions are identified as the two key driving factors behind the observed O episodes in LIS.

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.

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

Air pollution associated with wildfire smoke transport during the summer can significantly affect ozone (O) 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.

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 (LUE).

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.