five

Observed and Model-Derived Ozone Production Efficiency over Urban and Rural New York State Atmosphere

收藏
NOAA Institutional Repository2023-03-03 更新2026-04-25 收录
下载链接:
https://doi.org/10.3390/atmos8070126
下载链接
链接失效反馈
官方服务:
资源简介:
This study examined the model-derived and observed ozone production efficiency (OPE = ∆Ox/∆NOz) in one rural location, Pinnacle State Park (PSP) in Addison, New York (NY), and one urban location, Queens College (QC) in Flushing, NY, in New York State (NYS) during photo-chemically productive hours (11 a.m.–4 p.m. Eastern Standard Time (EST)) in summer 2016. Measurement data and model predictions from National Oceanic and Atmospheric Administration National Air Quality Forecast Capability (NOAA NAQFC)—Community Multiscale Air Quality (CMAQ) model versions 4.6 (v4.6) and 5.0.2 (v5.0.2) were used to assess the OPE at both sites. CMAQ-predicted and observed OPEs were often in poor agreement at PSP and in reasonable agreement at QC, with model-predicted and observed OPEs, ranging from approximately 5–11 and 10–13, respectively, at PSP; and 4–7 and 6–8, respectively, at QC. The observed relationship between OPE and oxides of nitrogen (NOx) was studied at PSP to examine where the OPE downturn may have occurred. Summer 2016 observations at PSP did not reveal a distinct OPE downturn, but they did indicate that the OPE at PSP remained high (10 or greater) regardless of the [NOx] level. The observed OPEs at QC were found by using species-specific reactive odd nitrogen (NOy) instruments and an estimated value for nitrogen dioxide (NO2), since observed OPEs determined using non-specific NOx and NOy instruments yielded observed OPE results that (1) varied from approximately 11–25, (2) sometimes had negative [NOz] concentrations, and (3) were inconsistent with CMAQ-predicted OPE. This difference in observed OPEs at QC depending on the suite of instruments used suggests that species-specific NOx and NOy instruments may be needed to obtain reliable urban OPEs.
提供机构:
NOAA
创建时间:
2023-03-03
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作