RESOLVE (Research on Operations-Limiting Visual Extinction) Monitoring Program Plan Evaluation Using Historical Data and Error Analysis.

Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

BackgroundAsthma is the most important chronic disease of childhood. The U.S. Environmental Protection Agency has concluded that children with asthma continue to be susceptible to ozone-associated adverse effects on their disease.ObjectivesThis study was designed to evaluate time trends in associations between declining warm-season O3 concentrations and hospitalization for asthma in children.MethodsWe undertook an ecologic study of hospital discharges for asthma during the high O3 seasons in California’s South Coast Air Basin (SoCAB) in children who ranged in age from birth to 19 years from 1983 to 2000. We used standard association and causal statistical analysis methods. Hospital discharge data were obtained from the State of California; air pollution data were obtained from the California Air Resources Board, and demographic data from the 1980, 1990, and 2000 U.S. Census. SoCAB was divided into 195 spatial grids, and quarterly average O3, sulfurdioxide, particulate matter with aerodynamic diameter ≤ 10 μm, nitrogen dioxide, and carbon monoxide were assigned to each unit for 3-month periods along with demographic variables.ResultsO3 was the only pollutant associated with increased hospital admissions over the study period. Inclusion of a variety of demographic and weather variables accounted for all of the non-O3 temporal changes in hospitalizations. We found a time-independent, constant effect of ambient levels of O3 and quarterly hospital discharge rates for asthma. We estimate that the average effect of a 10-ppb mean increase in any given mean quarterly 1-hr maximum O3 over the 18-year median of 87.7 ppb was a 4.6% increase in the same quarterly outcome.ConclusionsOur data indicate that at current levels of O3 experienced in Southern California, O3 contributes to an increased risk of hospitalization for children with asthma.

Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence

In California, emission control strategies have been implemented to reduce air pollutants. Here we estimate the changes in nitrogen oxides (NOx = NO + NO2) emissions in 2005–2010 using a state‐of‐the‐art four‐dimensional variational approach. We separately and jointly assimilate surface NO2 concentrations and tropospheric NO2 columns observed by Ozone Monitoring Instrument (OMI) into the regional‐scale Sulfur Transport and dEposition Model (STEM) chemical transport model on a 12 × 12 km2 horizontal resolution grid in May 2010. The assimilation generates grid‐scale top‐down emission estimates, and the updated chemistry fields are evaluated with independent aircraft measurements during the NOAA California Nexus (CalNex) field experiment. The emission estimates constrained only by NO2 columns, only by surface NO2, and by both indicate statewide reductions of 26%, 29%, and 30% from ~0.3 Tg N/yr in the base year of 2005, respectively. The spatial distributions of the emission changes differ in these cases, which can be attributed to many factors including the differences in the observation sampling strategies and their uncertainties, as well as those in the sensitivities of column and surface NO2 with respect to NOx emissions. The updates in California's NOx emissions reduced the mean error in modeled surface ozone in the Western U.S., even though the uncertainties in some urban areas increased due to their NOx‐saturated chemical regime. The statewide reductions in NOx emissions indicated from our observationally constrained emission estimates are also reflected in several independently developed inventories: ~30% in the California Air Resources Board bottom‐up inventory, ~4% in the 2008 National Emission Inventory, and ~20% in the annual mean top‐down estimates by Lamsal et al. using the global Goddard Earth Observing System (GEOS)‐Chem model and OMI NO2 columns. Despite the grid‐scale differences among all top‐down and bottom‐up inventories, they all indicate stronger emission reductions in the urban regions. This study shows the potential of using space‐/ground‐based monitoring data and advanced data assimilation approach to timely and independently update NOx emission estimates on a monthly scale and at a fine grid resolution. The well‐evaluated results here suggest that these approaches can be applied more broadly.

The purpose of this study was to study the spatial patterns of ambient air quality in Delhi in the absence of extensive datasets needed for space-time modeling. A spatial classification was attempted on the basis of ambient air quality data of nine years (1998 is latest year for which published data were available) for three criteria pollutants--nitrogen dioxide, sulfur dioxide, and suspended particulate matter. Monitoring stations take 24-hour samples twice a week. Published monthly average concentration data were used in this study. A hierarchical agglomerative algorithm using the average linkage between groups method and the Euclidean distance metric was used. Cluster analysis indicated that till 1998, by and large, two distinct classes existed. The results of cluster analysis prompted an investigation of systematic biases in the monitored data. No statistically significant differences in the mean concentration of all pollutants were observed between stations belonging to different land-use types (residential and industrial). This fact would be useful, if and when the authorities consider modifying the network or expanding it in Delhi. The results also support the recommendation that Delhi have a uniform standard across all areas. This study has provided a methodology for Indian researchers and practitioners to do an exploratory study of spatial patterns of air pollution and data quality issues in Indian cities using the National Ambient Air Quality Monitoring System data.

Air quality monitoring has been lacking in the rural and western North West Province. Here ambient sulphur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) concentrations, monitored with passive samplers at 10 sites, are presented. Widespread SO2 and NO2 problems weren’t observed. However, regular O3 standard limit exceedances are likely across the province. Increased SO2 and NO2 concentrations in the colder and drier months were evident. Inversion layer trapping of low-level emissions during the colder months and open biomass burning in the drier months increased ground level pollutant concentrations. Wet deposition of SO2 and NO2, and enhanced SO2 conversion to particulate sulphate, result in lower wet season concentrations. O3 concentrations were lower from May to July and higher from August to March. Three phenomena contributed to this. Firstly, shorter daylight hours (less photochemistry) and secondly, lower biogenic volatile organic compound (O3 precursors) concentrations during the colder months. Thirdly, the late winter/early spring open biomass burning peak lead to elevated carbon monoxide (CO) concentration (also an O3 precursor). Spatial patterns indicated higher SO2 concentrations in the west, due mainly to industrial emissions. The NO2 spatial map indicated two areas of higher concentration, i.e. Bapong in the east due mainly to industrial emissions, and Taung with its higher population density. The O3 spatial map was almost the inverse of NO2. The lower O3 and higher NO2 around Taung showed that O3 is titrated there. Additionally, the results indicate that non-point source emissions of NO2 are high enough to results in exceedances of the O3 standard limit. Overlay back trajectory maps showed that sites in the east are more frequently impacted by pollution transported from the Mpumalanga Highveld, Vaal Triangle and the Johannesburg-Pretoria megacity if compared to the west. Conversely, cleaner air masses impact the west more than sites in the east.

Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NOX) estimates from annual photochemical simulations for years 2002–2016 are compared to surface network measurements of NOX and total gas-phase-oxidized reactive nitrogen (NOY) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NOX performance. Summertime NOX is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NOX bias dropped from between 28% and 57% for earlier years (2002–2012) to between −2% and 7% for later years (2013–2016). Summer daytime NOX tends to be unbiased or underpredicted. In winter, the evening NOX overpredictions remain, but NOX is unbiased or underpredicted overnight, in the morning, and during the day. NOX overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NOX. Modeled NOX performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NOX emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NOY but do not improve summertime NOX bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NOX performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.

Theoretical relationships have been derived and transformed into a computer model to describe plume visual impact at various observer vantage points resulting from power plant boiler operation. Plume visual impact results from a reduction in visual range (visibility) and plume coloration. The model considers plume transport and diffusion, light scattering and absorption by aerosols and gases, and chemical transformation in the plume of nitric oxide to nitrogen dioxide, sulfur dioxide to sulfates, and nitrogen oxides to nitrates. The model is applied in a parametric study to explore the effect of emissions and environmental conditions on plume visual impact. The case selected is a hypothetical 1500 Mwe coal-fired power plant equipped with efficient particulate abatement equipment. The results establish that visual range is not significantly affected unless the observer is viewing along the plume axis or unless significant amounts of secondary particles are formed in the plume. Yellow-brown plume coloration is shown to result from the net production of nitrogen dioxide gas in the plume. The coloration is most apparent during stable and clear atmospheric conditions and effectively masked for elevated concentrations of background or plume aerosol.

An error analysis method based on historical orbital data is introduced, the periodic characteristics of the orbit prediction error are presented, and the error fitting is done by bringing in the Poisson series.

The U.S. Environmental Protection Agency (EPA) is involved in the discovery, evaluation, and application of low-cost air quality (AQ) sensors to support citizen scientists by directly engaging with them in the pursuit of community-based interests. The emergence of low-cost (<$2500) sensors have allowed a wide range of stakeholders to better understand local AQ conditions. Here we present results from the deployment of the EPA developed Citizen Science Air Monitor (CSAM) used to conduct approximately five months (October 2016–February 2017) of intensive AQ monitoring in an area of Puerto Rico (Tallaboa-Encarnación, Peñuelas) with little historical data on pollutant spatial variability. The CSAMs were constructed by combining low-cost particulate matter size fraction 2.5 micron (PM2.5) and nitrogen dioxide (NO2) sensors and distributed across eight locations with four collocated weather stations to measure local meteorological parameters. During this deployment 1 h average concentrations of PM2.5 and NO2 ranged between 0.3 to 33.6 µg/m3 and 1.3 to 50.6 ppb, respectively. Peak concentrations were observed for both PM2.5 and NO2 when conditions were dominated by coastal-originated winds. These results advanced the community’s understanding of pollutant concentrations and trends while improving our understanding of the limitations and necessary procedures to properly interpret measurements produced by low-cost sensors.

The reaction of OH with NO2 is pivotal in both stratospheric and tropospheric chemistry; in each case it is the dominant homogeneous mechanism for conversion of NOx to NOy. The rate constant is a strong function of pressure and temperature, and key portions of the pressure‐temperature domain are poorly or ambiguously covered by the available data. These include conditions typical of the tropospheric boundary layer and of the lower stratosphere. At surface conditions differences of 60% exist both in the literature data and between the major recommendations, while at lower stratospheric conditions there are few available data. Our High Pressure Flow kinetics system is ideally suited to studying this reaction, as we are able to scan both temperature and pressure while maintaining wall‐less conditions, eliminating the possible complications of heterogeneous chemistry. Here we report a temperature‐ and pressure‐dependent study (220–300 K, 50–150 torr) of this reaction; the measured rate constants are in excellent agreement with previously published values down to 240 K, but lie 10–20% lower than the historical data available below that temperature. An analysis of all available data motivates a large (∼ 20%) downward revision in the recommended rate constant below room temperature.

Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study

This thesis provides an evaluation of Air Quality in selected areas of Sydney and Kigali using historical and measured data, respectively. In addition to the evaluation, the obtained data is used to develop mathematical models for the prediction of dependent air pollutants using Multi Linear and Principal Component Regression. Historical data from monitoring stations for four pollutants, PM2.5, PM10, NO2 (nitrogen dioxide), and SO2 (sulphur dioxide), in North Parramatta was obtained from the Department of Planning, Industry and Environment. A comparative analysis was conducted to evaluate the levels of these pollutants with respect to Australia and World Health Organisation Air Quality Standards in the periods before (2018 and 2019), during (2020 and 2021), and after (2022, 2023 and 2024) the COVID-19 pandemic. The comparative analysis showed that in the pre Covid-19 period, PM2.5 and PM10 exceeded the recommended levels in 438 (2018 and 2019) recordings. This level decreased to 164 times during COVID-19 times (2020 and 2021) and continued to decrease post Covid-19 (2022 and 2023) to 103 times. Although the data shows a trend of decrease in number of times the pollutants exceeded the recommended values as well as the recorded spikes reduction in pollution patterns compared to pre and during COVI-19 times, Parramatta North Air Quality still experiences significant pollutant spikes as evidenced by recorded data for the period under research. During the pandemic times (2020 and 2021) the pollution frequency reduced but the spikes persisted. In 2020, PM2.5 and PM10 recorded maximum values of 170.9 μg/m³ and 803.7 μg/m³ which are 6.8 times and 16 times the recommended values, respectively. In 2021 and 2022, these maximum values of PM2.5 and PM10 decreased to 101.4 μg/m³ and 185.2 μg/m³, respectively. Nevertheless, these values are still 4 times and 7.4 times the recommended values, respectively. Finally, the acquired data for North Parramatta and Kigali were used to develop mathematical models to predict the levels of f PM10 as a dependent pollutant from levels of PM2.5, SO2, NO2, and PM1 as independent pollutants. For Parramatta North, a Principal Component Regression model was used to predict PM10 against PM2.5, SO2, and NO2 while for Kigali, a Multi Linear Regression model was used to predict PM10 from levels of PM1 and PM2.5. Results of both models show a positive correlation between the dependent and independent pollutants.

The U.S. Environmental Protection Agency (EPA) and Desarrollo Integral del Sur, Inc (DISUR), a Puerto Rico-based community action group, collaborated to determine the efficacy of citizen science involving the use of low cost air quality sensors. The EPA developed a unique low cost AC powered multi-pollutant Citizen Science Air Monitor (CSAM) that was provided to the community group along with the training/tools needed for its operation. The citizens self-organized a community effort to conduct approximately five months of intensive air quality monitoring in an area of Puerto Rico (Tallaboa-Encarnaci&oacute;n, Pe&ntilde;uelas) having little historical data on spatial variability (Ponce). Real-time measurements of the particulate matter size fraction 2.5 micron (PM2.5), nitrogen dioxide (NO2), total volatile organic compounds (tVOCs), and meteorological parameters (wind speed, wind directions, temperature, relative humidity) were obtained. The study provided the Pe&ntilde;uelas and surrounding communities an in-depth investigation of local air quality and opportunities for citizen scientists to gain extensive experience in the use of emerging sensor technologies. The collaboration also provided the EPA an opportunity to evaluate low cost sensor performance under harsh environmental conditions (high relative humidity in a coastal environment). We present the approach and preliminary environmental findings of the EPA&rsquo;s efforts the deploy a low cost multi-pollutant sensor pod associated with a citizen science research study.&nbsp;

Transient permeation of oxygen, nitrogen, and carbon dioxide through certain cellulose acetate (CA) dense membrances made from solutions in dioxane and dioxane‐added ethanol were studied by the flow method. In order to explain the overshoots for transient permeation rates in the latter case, a new model is proposed. It postulates a time‐dependence solubility coefficient, in the form of a series of exponential terms, which is combined in Fick's second law and solved. The model fits well the obtained permeation kinetics, leading to values of the diffusion, and permeation coefficients, and the structure change‐related time. The overshoots are explained by the formation of unstable structure from dioxane‐ethanol solvent mixtures, a memory effect of aggregated chains, which undergoes consolidation upon gas penetration. The stable state which results from the consolidation process is the same as that of membrances obtained from pure dioxane solutions. © 1994 John Wiley &amp; Sons, Inc.