Improved daily precipitation nitrate and ammonium concentration models for the Chesapeake Bay Watershed

Daily precipitation nitrate and ammonium concentration models were developed for the Chesapeake Bay Watershed (USA) using a linear least-squares regression approach and precipitation chemistry data from 29 National Atmospheric Deposition Program/National Trends Network (NADP/NTN) sites. Only weekly samples that comprised a single precipitation event were used in model development. The most significant variables in both ammonium and nitrate models included: precipitation volume, the number of days since the last event, a measure of seasonality, latitude, and the proportion of land within 8km covered by forest or devoted to industry and transportation. Additional variables included in the nitrate model were the proportion of land within 0.8km covered by water and/or forest. Local and regional ammonia and nitrogen oxide emissions were not as well correlated as land cover. Modeled concentrations compared very well with event chemistry data collected at six NADP/AirMoN sites within the Chesapeake Bay Watershed. Wet deposition estimates were also consistent with observed deposition at selected sites. Accurately describing the spatial distribution of precipitation volume throughout the watershed is important in providing critical estimates of wet-fall deposition of ammonium and nitrate.     

Spatial variation of particle number and mass over four European cities

The number of ultrafine particles may be a more health relevant characteristic of ambient particulate matter than the conventionally measured mass. Epidemiological time series studies typically use a central site to characterize human exposure to outdoor air pollution. There is currently very limited information how well measurements at a central site reflect temporal and spatial variation across an urban area for particle number concentrations (PNC).The main objective of the study was to assess the spatial variation of PNC compared to the mass concentration of particles with diameter less than 10 or 2.5 μm (PM₁₀ and PM_2.5).Continuous measurements of PM₁₀, PM_2.5, PNC and soot concentrations were conducted at a central site during October 2002–March 2004 in four cities spread over Europe (Amsterdam, Athens, Birmingham and Helsinki). The same measurements were conducted directly outside 152 homes spread over the metropolitan areas. Each home was monitored during 1 week. We assessed the temporal correlation and the variability of absolute concentrations.For all particle indices, including particle number, temporal correlation of 24-h average concentrations was high. The median correlation for PNC per city ranged between 0.67 and 0.76. For PM_2.5 median correlation ranged between 0.79 and 0.98. The median correlation for hourly average PNC was lower (range 0.56–0.66). Absolute concentration levels varied substantially more within cities for PNC and coarse particles than for PM_2.5. Measurements at the central site reflected the temporal variation of 24-h average concentrations for all particle indices at the selected homes across the urban area. A central site could not assess absolute concentrations across the urban areas for particle number.     

The accuracy of plume trajectories forecast using the U.K. Meteorological Office operational forecasting models and their sensitivity to calculation schemes

Studies into the various errors associated with long-range 950 mb trajectories computed using the Meteorological Office numerical weather prediction suite are presented. In the absence of observational data, trajectory error is measured using a verification computed from the model analysed and short-period forecast wind fields. While it is safest to assume that errors determined in this way are the minimum to be expected in relation to “true” trajectories, they nonetheless provide a useful diagnostic of model performance under different synoptic conditions and for trajectory releases at different stages of the forecast. Absolute and directional errors are analysed and the effect of altering the trajectory calculation schemes considered. The possible implications of the results for acid precipitation control strategies are discussed in some detail.     

Spatial and temporal trends of precipitation chemistry in the United States, 1985-2002

A Seasonal Kendall Trend (SKT) test was applied to precipitation-weighted concentration data from 164 National Atmospheric Deposition Program National Trends Network (NADP/NTN) sites operational from 1985 to 2002. Analyses were performed on concentrations of ammonium, sulfate, nitrate, dissolved inorganic nitrogen (DIN, sum of nitrogen from nitrate and ammonium), and earth crustal cations (ECC, sum of calcium, magnesium, and potassium). Over the 18-year period, statistically significant (p< or =0.10) increases in ammonium concentrations occurred at 93 sites (58%), while just three sites had statistically significant ammonium decreases. Central and northern Midwestern states had the largest ammonium increases. The generally higher ammonium concentrations were accompanied by significant sulfate decreases (139 sites, 85%), and only one significant increase which occurred in Texas. In the west central United States there were significant nitrate increases (45 sites, 27%), while in the northeastern United States there were significant decreases (25 sites, 15%). Significant DIN decreases were observed in the northeastern United States (11 sites, 7%); elsewhere there were significant increases at 75 sites (46%). ECC decreased significantly at 65 sites (40%), predominantly in the central and southern United States, and increased at 11 sites (7%). The concentrations of sulfate, nitrate, and ammonium in precipitation have changed markedly over the time period studied. Such trends indicate changes in the mix of gases and particles scavenged by precipitation, possibly reflecting changes in emissions, atmospheric chemical transformations, and weather patterns.     

The relationship between hydrogen and sulphate ions in precipitation-A numerical analysis of rain and snowfall chemistry

Acidic rain has been identified as potentially harmful to the aquatic and terrestrial components of the ecosystem. Sulphate measured in rain and snow has been used as a surrogate indicator of acidic deposition. If sulphur dioxide controls are the means to limit acidic deposition, then the association between sulphate and hydrogen ion concentrations in precipitation is an important factor in establishing such limits. Selected data on rain and snowfall chemistry from the National Atmospheric Deposition Program (NADP), the Electric Power Research Institute's SURE, the utility industries' UAPSP, and the Department of Energy's MAP3S were reviewed. Numerical analyses were performed to assess the relationship between hydrogen and sulphate ion concentrations. The strength of the association between hydrogen and sulphate ions varied from site to site. In the Midwestern and Eastern regions, the Pearson correlation coefficient was over 0.50 while in the Central and Upper Midwestern parts of the United States, the correlation coefficients were less than 0.25. Regardless of the strength of the association between hydrogen and sulphate ions, all but one of the NADP/NTN sites used in our analysis exhibited at least 30% of the anions (sulphate, nitrate, and chloride) associated with cations other than hydrogen. For sites where the strength of the association was weak, between 65% and 98% of the anions appeared to be associated with cations other than hydrogen. Because a large percentage of the anions (i.e. sulphate, nitrate, and chloride) appear to be associated with cations other than hydrogen even at those sites where the association between hydrogen and sulphate ions was strong, the complex chemistry controlling the acidity in precipitation may make it difficult to predict the impact of a reduction in sulphate concentration.     

Seasonal variability in CH4 emissions from a landfill in a cool, semiarid climate

Methane exchange with the atmosphere was measured during three seasons at the Rooney Road landfill in Jefferson County, CO. Substantial spatial and temporal variability in exchange rates were observed. Mean fluxes to the atmosphere were 534, 1290, and 538 mg CH4/m2/day, respectively, in the fall of 1994, winter of 1994-1995, and summer of 1995. Median fluxes were 12.42, 8.62, and 5.65 mg CH4/m2/day, respectively, during those seasons. Forty-three of 177 measurements had small negative fluxes, suggesting methanotrophic activity in the landfill cover soils. Despite probable methanotrophic activity in cover soils, landfills without gas collection systems may emit substantial CH4 to the atmosphere, with large spatial and seasonal variability.     

Evaluation of precipitation chemistry siting criteria using paired stations from northern Maine and southeastern Texas

Two-year precipitation chemistry data records from each of the two paired stations are compared to determine whether the precipitation chemistry is different. In each pair, one of the stations (Caribou, Maine and Victoria, Texas) violates National Atmospheric Deposition Program (NADP) siting requirements, while the other station (Presque Isle, Maine and Beeville, Texas) is in compliance. Methods employed to carry out this study include the calculation of precipitation weighted statistics, the study of logarithmic distributions, the study of regression line residuals, the study of relative sample differences and the use of the non-parametric Wilcoxon test for two matched samples. Results indicate no clear differences occurred between paired stations over a seasonal or longer period of time; however, considerable variation was observed on a weekly basis. Maine stations were in excellent agreement for all ions; Texas stations showed small differences between ions typically associated with soil dust and between ions typically associated with anthropogenic activity.     

Transport and dispersion during wintertime particulate matter episodes in the San Joaquin Valley, California

Data analysis and modeling were performed to characterize the spatial and temporal variability of wintertime transport and dispersion processes and the impact of these processes on particulate matter (PM) concentrations in the California San Joaquin Valley (SJV). Radar wind profiler (RWP) and radio acoustic sounding system (RASS) data collected from 18 sites throughout Central California were used to estimate hourly mixing heights for a 3-month period and to create case studies of high-resolution diagnostic wind fields, which were used for trajectory and dispersion analyses. Data analyses show that PM episodes were characterized by an upper-level ridge of high pressure that generally produced light winds through the entire depth of the atmospheric boundary layer and low mixing heights compared with nonepisode days. Peak daytime mixing heights during episodes were -400 m above ground level (agl) compared with -800 m agl during nonepisodes. These episode/nonepisode differences were observed throughout the SJV. Dispersion modeling indicates that the range of influence of primary PM emitted in major population centers within the SJV ranged from -15 to 50 km. Trajectory analyses revealed that little intrabasin pollutant transport occurred among major population centers in the SJV; however, interbasin transport from the northern SJV and Sacramento regions into the San Francisco Bay Area (SFBA) was often observed. In addition, this analysis demonstrates the usefulness of integrating RWP/RASS measurements into data analyses and modeling to improve the understanding of meteorological processes that impact pollution, such as aloft transport and boundary layer evolution.     

Operator-splitting errors in coupled reactive transport codes for transient variably saturated flow and contaminant transport in layered soil profiles

One possible way of integrating subsurface flow and transport processes with (bio)geochemical reactions is to couple by means of an operator-splitting approach two completely separate codes, one for variably-saturated flow and solute transport and one for equilibrium and kinetic biogeochemical reactions. This paper evaluates the accuracy of the operator-splitting approach for multicomponent systems for typical soil environmental problems involving transient atmospheric boundary conditions (precipitation, evapotranspiration) and layered soil profiles. The recently developed HP1 code was used to solve the coupled transport and chemical equations. For steady-state flow conditions, the accuracy was found to be mainly a function of the adopted spatial discretization and to a lesser extent of the temporal discretization. For transient flow situations, the accuracy depended in a complex manner on grid discretization, time stepping and the main flow conditions (infiltration versus evaporation). Whereas a finer grid size reduced the numerical errors during steady-state flow or the main infiltration periods, the errors sometimes slightly increased (generally less than 50%) when a finer grid size was used during periods with a high evapotranspiration demand (leading to high pressure head gradients near the soil surface). This indicates that operator-splitting errors are most significant during periods with high evaporative boundary conditions. The operator-splitting errors could be decreased by constraining the time step using the performance index (the product of the grid Peclet and Courant numbers) during infiltration, or the maximum time step during evapotranspiration. Several test problems were used to provide guidance for optimal spatial and temporal discretization.     

Variation of wet deposition chemistry in Sequoia National Park,California

Sequoia National Park has monitored wet deposition chemistry in conjunction with the National Atmospheric Deposition Program and National Trends Network (NADP/NTN), on a weekly basis since July, 1980. Annual deposition of H, NO₃ and SO₄ (0.045, 3.6, and 3.9 kg ha⁻¹ a⁻¹, respectively) is relatively low compared to that measured in the eastern United States, or in the urban Los Angeles and San Francisco areas. Weekly ion concentrations are highly variable. Maximum concentrations of 324,162, and 156 μeq ol⁻¹ of H, NO₃ and SO₄ have been recorded for one low volume summer storm (1.4 mm). Summer concentrations of NO₃ and SO₄ average two and five times higher, respectively, than concentrations reported for remote areas in the world. There is considerable variability in the ionic concentration of low volume samples, and much less variability in moderate and high volume samples.     

Assessment of boundary layer variations in the Tampa Bay Area during the Bay Region Atmospheric Chemistry Experiment (BRACE)

The National Oceanic and Atmospheric Administration's (NOAA's) Air Resources Laboratory (ARL), using the NOAA Twin Otter aircraft, made meteorological and chemical measurements during 21 flights in May 2002 in and around the Tampa Bay, Florida area as part of the Bay Region Atmospheric Chemistry Experiment (BRACE). One or more vertical profiles were flown during each flight both over land and over the Gulf of Mexico. NOAA's Environmental Technology Laboratory (ETL; now part of NOAA's Earth System Research Laboratory (ESRL)) deployed three surface-based 915-MHz radar wind profilers equipped with radio acoustic sounding systems (RASS) at Ruskin, Sydney, and St. Petersburg. The National Weather Service Office in Tampa (NWS/TBW) released rawinsondes twice daily from the Ruskin site. The measurements of temperature, dew point, potential temperature, ozone, and condensation nuclei acquired during the aircraft profiles are analyzed, and in combination with the profiler and sounding data, are used to determine the structure of the boundary layer over the Tampa Bay region and the temporal and spatial changes that occurred in that structure during representative flights.     

Comparative statistical study of hourly precipitation determined by radar-based Stage IV and ground-based methods in the North Central United States

Detailed hourly precipitation data are required for long-range modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants using the CALPUFF model. In sparsely populated areas such as the north central United States, ground-based precipitation measurement stations may be too widely spaced to offer a complete and accurate spatial representation of hourly precipitation within a modeling domain. The availability of remotely sensed precipitation data by satellite and the National Weather Service array of next-generation radars (NEXRAD) deployed nationally provide an opportunity to improve on the paucity of data for these areas. Before adopting a new method of precipitation estimation in a modeling protocol, it should be compared with the ground-based precipitation measurements, which are currently relied upon for modeling purposes. This paper presents a statistical comparison between hourly precipitation measurements for the years 2006 through 2008 at 25 ground-based stations in the north central United States and radar-based precipitation measurements available from the National Center for Environmental Predictions (NCEP) as Stage IV data at the nearest grid cell to each selected precipitation station. It was found that the statistical agreement between the two methods depends strongly on whether the ground-based hourly precipitation is measured to within 0.1 in/hr or to within 0.01 in/hr. The results of the statistical comparison indicate that it would be more accurate to use gridded Stage IV precipitation data in a gridded dispersion model for a long-range simulation, than to rely on precipitation data interpolated between widely scattered rain gauges.

Implications:

The current reliance on ground-based rain gauges for precipitation events and hourly data for modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants results in potentially large discontinuity in data coverage and the need to extrapolate data between monitoring stations. The use of radar-based precipitation data, which is available for the entire continental United States and nearby areas, would resolve these data gaps and provide a complete and accurate spatial representation of hourly precipitation within a large modeling domain.     

Spatial and seasonal variation of PM10 mass concentrations in Taiwan

Hourly data of PM10 concentration collected from an air quality-monitoring network has been analyzed over Taiwan from 1994 to 1999. Fourteen sites from 72 monitoring stations were selected to evaluate the spatial and seasonal variations in the regions of north, southwest, south, east and National Park. The selected monitoring sites are located in a suburban environment, except Nantz and Linyuan that are located in industrial areas. Moreover, Yangming and Hengchuen are located in National Park. Spatial and seasonal variations of PM10 concentrations are rather large over Taiwan. Annual average in south is approximately six times higher than in National parks. In northern sites, the highest concentration occurs in March–May, which is attributed to the occurrence of dust storms in arid regions of central Asia and the transport of dust by northeasterly monsoon. A marked seasonal variation of PM10 concentrations can be observed both in southwestern and southern regions. The pattern is characterized by high concentrations in winter and low in summer. Appearance of the highest monthly PM10 concentration in winter of south may be in part due to the lowest number of monthly precipitation days and low temperature, both of which occurred in winter. The frequency of PM10 daily mean concentration for exceeded 150 μg m⁻³ is 15% during winter in south, which reflects the serious pollution problem there. Monitoring sites in National Park are representatives of remote environments, but the PM10 concentrations are still affected by the dust storms and human activities.     

Monthly and annual bias in weekly (NADP/NTN) versus daily (AIRMoN) precipitation chemistry data in the Eastern USA

Previous comparisons of the data from the National Atmospheric Deposition Program, National Trends Network (NTN) against collocated event sampled data and daily sampled data suggest a substantial bias in the concentration of ammonium [NH₄⁺] and concentrations of several base cations, while the comparability of other ion concentrations ranges among the studies. Eight years of collocated data from five NTN and Atmospheric Integrated Research and Monitoring Network (AIRMoN) sites are compared here. Unlike previous analyses, the data from these two data sets were analyzed in the same laboratory using the same analytical methods; therefore, factors that influence concentration differences can be isolated to sampling frequency and sample preservation techniques. For comparison, the relative biases for these data have been calculated using both median value and volume-weighted mean concentrations, following two different approaches in the literature. The results suggest a relative bias of about 10% in [NH₄⁺] (NTN less than AIRMoN), which is smaller than previous estimates that included the influence of inter-laboratory comparisons. The annual relative bias of [H⁺] increases over the analysis period, which results in a larger total relative bias for [H⁺] than found in a previous analysis of AIRMoN and NTN data. When comparing NTN and AIRMoN data on monthly time scales, strong seasonal variations are evident in the relative bias for [H⁺], [NH₄⁺], and [SO₄²⁻]. Large biases in [SO₄²⁻] (NTN greater than AIRMoN) on monthly times scales have not been detected in previous analyses where data for all seasons were considered together.     

Temporal variability of the precipitation chemistry in the forest area of Istanbul, Turkey

The chemical compositions of precipitation show temporal and spatial variability. It is important to determine the temporal variation of the chemical composition of rainwater for estimating the impacts of pollutants on the forest. In this study, the 34 rainwater samples are collected using, for the first time, the specially designed collectors between November 1997 and March 1998 in Istanbul University, Faculty of Forestry at Bahcekoy, Istanbul in Belgrad Forest. The sequentially collected samples are analysed for major ions concentrations and pH estimations. The pH values for all samples vary from 5.1-7.6. In general, the concentration of all rainwater samples decrease with time. Furthermore, it is estimated that the average Ca²⁺) (1943.0 µeql^-1) and SO₄^2- (887.3 µeql^-1) concentrations are extremely high during the first ten minutes of the precipitation event.     

Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle, Washington

This study investigates how PM2.5 varies spatially and how these spatial characteristics can be used to identify potential monitoring sites that are most representative of the overall ambient exposures to PM2.5 among susceptible populations in the Seattle, WA, area. Data collected at outdoor sites at the homes of participants of a large exposure assessment study were used in this study. Harvard impactors (HIs) were used at 40 outdoor sites throughout the Seattle metropolitan area. Up to six sites at a time were monitored for 10 consecutive 24-hr average periods. A fixed-effect analysis of variance (ANOVA) model that included date and location effects was used to analyze the spatial variability of outdoor PM2.5 concentrations. Both date and location effects were shown to be highly significant, explaining 92% of the variability in outdoor PM2.5 measurements. The day-to-day variability was 10 times higher than the spatial variability between sites. The site mean square was more than twice the error mean square, showing that differences between sites, while modest, are potentially an important contribution to measurement error. Variances of the model residuals and site effects were examined against spatial characteristics of the monitoring sites. The spatial characteristics included elevation, distance from arterials, and distance from major PM2.5 point sources. Results showed that the most representative PM2.5 sites were located at elevations of 80-120 m above sea level, and at distances of 100-300 m from the nearest arterial road. Location relative to industrial PM2.5 sources is not a significant predictor of residential outdoor PM2.5 measurements. Additionally, for sites to be representative of the average population exposures to PM2.5 among those highly susceptible to the health effects of PM2.5, areas of high elderly population density were considered. These representative spatial characteristics were used as multiple, overlapping criteria in a Geographic Information System (GIS) analysis to determine where the most representative sites are located.     

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.     

Trend,seasonal and multivariate analysis study of total gaseous mercury data from the Canadian atmospheric mercury measurement network (CAMNet)

Long-term monitoring data of total gaseous mercury (TGM) concentrations from the Canadian Atmospheric Mercury Measurement Network (CAMNet) were analysed for temporal trends, seasonality and comparability within the network and compared to other network and model results. Data collected from 11 Canadian measurement sites between 1995 and 2005 were analysed. Sites within CAMNet were characterized by principle component analysis (PCA) into four main categories. For the first time since automated TGM measurements have been made within CAMNet, this paper reveals statistically significant decreasing TGM concentrations from rural locations in Canada during this time period. The largest declines were observed close to the urban areas of Toronto and Montreal, where levels fell by 17% at Point Petre, and 13% at St. Anicet, respectively. Many of the TGM changes are comparable with the overall trends observed in total mercury concentrations in precipitation, for similar time periods, at co-located or nearby National Atmospheric Deposition programme's Mercury Deposition Network (NADP-MDN) sites. The results show that these changes are mostly driven by local or regional changes in mercury emissions. Other sites within CAMNet reflect reported changes in hemispherical global background concentrations of airborne mercury, where slight decreases or no statistically significant trend in TGM concentrations exist over the same time period.     

Assessing the impact of differential measurement error on estimates of fine particle mortality

In air pollution epidemiology, error in measurements of correlated pollutants has been advanced as a reason to distrust regressions that find statistically significant weak associations. Much of the related debate in the literature and elsewhere has been qualitative. To promote quantitative evaluation of such errors, this paper develops an air pollution time-series model based on correlations among unit-normal variables. Assuming there are no other sources of bias present, the model shows the expected amount of relative bias in the regression coefficients of a bivariate regression of coarse and fine particulate matter measurements on daily mortality. The model only requires information on instrumental error and spatial variability, along with the observed regression coefficients and information on the true fine-course correlation. Analytical results show that if one pollutant is truly more harmful than the other, then it must be measured more precisely than the other in order not to bias the ratio of the fine and course regression coefficients. Utilizing published data, a case study of the Harvard Six-Cities study illustrates use of the model and emphasizes the need for data on spatial variability across the study area. Current epidemiology time-series regressions can use this model to address the general concern of correlated pollutants with differing measurement errors.     

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind