A new method to estimate air-quality levels using a synoptic-regression approach. Part I: Present-day O3 and PM10 analysis

In order to make projections for future air-quality levels, a robust methodology is needed that succeeds in reconstructing present-day air-quality levels. At present, climate projections for meteorological variables are available from Atmospheric-Ocean Coupled Global Climate Models (AOGCMs) but the temporal and spatial resolution is insufficient for air-quality assessment. Therefore, a variety of methods are tested in this paper in their ability to hindcast maximum 8 hourly levels of O₃ and daily mean PM₁₀ from observed meteorological data. The methods are based on a multiple linear regression technique combined with the automated Lamb weather classification. Moreover, we studied whether the above-mentioned multiple regression analysis still holds when driven by operational ECMWF (European Center for Medium-Range Weather Forecast) meteorological data. The main results show that a weather type classification prior to the regression analysis is superior to a simple linear regression approach. In contrast to PM₁₀ downscaling, seasonal characteristics should be taken into account during the downscaling of O₃ time series. Apart from a lower explained variance due to intrinsic limitations of the regression approach itself, a lower variability of the meteorological predictors (resolution effect) and model deficiencies, this synoptic-regression-based tool is generally able to reproduce the relevant statistical properties of the observed O₃ distributions important in terms of European air quality Directives and air quality mitigation strategies. For PM₁₀, the situation is different as the approach using only meteorology data was found to be insufficient to explain the observed PM₁₀ variability using the meteorological variables considered in this study.     

Modeling speciated terpenoid emissions from the European boreal forest

We present the first estimates of speciated monoterpene emissions from the North European coniferous forests. Measured emission factors and emission profiles of boreal tree species (Picea abies, Pinus sylvestris, Betula pendula, Salix phylicifolia, Populus tremula, and Alnus incana) were used together with detailed satellite land cover information and meteorological data in an emission model based on the Guenther emission algorithms. The variation of the coniferous biomass within the boreal region (60°N to 70°N) was obtained from forest inventory data, and the seasonal variability of the deciduous biomass was taken into account through simple boreal climatology parameterisation. The annual biogenic emissions in the boreal zone are dominated by coniferous species, but in the summer months, the deciduous contribution to the monoterpene and isoprene emissions is considerable. Norway spruce (Picea abies) is the most important isoprene emitter in the north European boreal forests. The biogenic emission fluxes in the South boreal zone are approximately twice as high as fluxes in the North boreal zone. α- and β-pinene, carene, and cineole are the most abundant emitted terpenes, with a strong contribution of isoprene and linalool during the summer months.     

Annual and seasonal variability of ambient aerosols over an urban region in western India

Annual and seasonal variabilities in source contribution to total suspended particles (TSP) measured over an urban location in western India, Ahmedabad between May 2000 and January 2003 are examined in this study. Positive matrix factorization (PMF) resolved six factors including airborne regional dust, calcium carbonate rich dust, biomass burning/vehicular emissions, secondary nitrate/sulfate, marine aerosol, and smelter. In this study, non-parametric statistical tests including the Kruskal–Wallis analysis of variance (K–W ANOVA) and Spearman rank correlation (ρ) test were used to assess the annual and seasonal variations in factor contributions, and the influence of meteorology on these contributions, respectively. None of the factor contributions exhibited annual variations except airborne regional dust, and biomass burning/vehicular emissions factors. All of the factors exhibited seasonal variations. Several factor monsoon (July–September) median concentrations were significantly different from one or more of the other season medians. In general, it appeared that meteorological factors played a role in establishing the seasonal behavior of factor contributions. Factor contributions exhibited low to moderate correlations with meteorological parameters such as temperature, relative humidity, wind direction, and wind speed. Amongst all of the relationships, marine aerosol factor was reasonably well correlated with relative humidity (ρ = 0.73) and wind direction (ρ = 0.73) during the pre-monsoon season (March–May). This observation suggests that the aerosol transported by moisture laden winds from the Arabian sea contribute to this factor. The airborne regional dust factor was also moderately correlated with wind speed (ρ = 0.70) during the post-monsoon season. This relationship indicates that high regional dust concentrations are favored by high wind speeds and the resultant increase in dispersion.     

Estimation of Trends in Atmospheric Concentrations of Sulfate in the Northeastern United States

Abstract

Daily atmospheric concentrations of sulfate collected at six locations in the northeastern United States are regressed against meteorological factors, ozone, seasonal cycles, and time in order to determine if a significant trend in sulfate can be detected. The data used in this analysis were collected during the Sulfate Regional Experiment (SURE, 1977-1978) and the Eulerian Model Evaluation Field Study (EMEFS, 1988-1989). Ozone, specific humidity, and seasonal terms (reflecting the potential of the atmosphere for oxidation of sulfur dioxide) emerged as important explanatory variables. After accounting for the variability explained by environmental factors, the median estimated change in sulfate concentration from the six locations over the 11-year period is -22% (or -28% if ozone is not used as an explanatory variable). Although there are wide variations among locations, these changes are commensurate with an estimated 25% decline in sulfur emissions in the northeastern U.S. during the same period. These analyses provide insight into methods for detecting reductions in sulfate that may be expected to occur as a result of the Clean Air Act Amendments of 1990. Uncertainties in the estimates, with consideration of serial correlation in the data, imply a minimum detectable reduction of 10% using this modeling procedure with similar data availability.     

Seasonal variations in sugar contents and microbial community in a ryegrass soil

The relationship among sugar concentrations, microbial community and physical variables (precipitation and soil temperature) was investigated in a ryegrass soil from January 2004 to January 2005. Mono- and disaccharide sugars were extracted using a mixture of dichloromethane and methanol and analyzed as their TMS derivatives by GC-MS. Changes in microbial community were assessed using phospholipid and neutral lipid fatty acids (PLFA and NLFA, respectively) analysis. The results of a one-year study showed that the seasonal variability of sugar contents found in the soil samples is mainly related to biomass or nutritional status of the fungal community. The increase in sucrose and fructose exudation by plant roots in the beginning of the growing season (early spring) may be responsible for the highest fungal biomass amount (PLFAs) observed in this study. Fungal storage lipid abundances (NLFAs) peaked in summer, during the same period that the highest concentrations of mannitol and trehalose were detected. This is consistent with these two sugars being stress-induced fungal metabolites, produced due to the low soil moisture observed during this season. In contrast, bacterial community growth seems to be more dependent on plant substrate than on physical variables, since the strongest decrease in bacterial biomass amounts (PLFAs) was found after cutting of the ryegrass field in early July.     

Temporal variability of mercury speciation in urban air

Semi-continuous measurements of ambient mercury (Hg) species were performed in Detroit, MI, USA for the calendar year 2003. The mean (±standard deviation) concentrations for gaseous elemental mercury (GEM), particulate mercury (HgP), and reactive gaseous mercury (RGM) were 2.2±1.3 ng m⁻³, 20.8±30.0, and 17.7±28.9 pg m⁻³, respectively. A clear seasonality in Hg speciation was observed with GEM and RGM concentrations significantly (p<0.001) greater in warm seasons, while HgP concentrations were greater in cold seasons. The three measured Hg species also exhibited clear diurnal trends which were particularly evident during the summer months. Higher RGM concentrations were observed during the day than at night. Hourly HgP and GEM concentrations exhibited a similar diurnal pattern with both being inversely correlated with RGM. Multivariate analysis coupled with conditional probability function analysis revealed the conditions associated with high Hg concentration episodes, and identified the inter-correlations between speciated Hg concentrations, three common urban air pollutants (sulfur dioxide, ozone, and nitric oxides), and meteorological parameters. This analysis suggests that both local and regional sources were major factors contributing to the observed temporal variations in Hg speciation. Boundary layer dynamics and the seasonal meteorological conditions, including temperature and moisture content, were also important factors affecting Hg variability.     

Modeling short-term variability of semivolatile organic chemicals in air at a local scale: an integrated modeling approach

Monitoring campaigns from different locations have recently shown how air concentrations of persistent semivolatile contaminants such as polychlorinated biphenyls (PCBs) often exhibit short-term (less than 24 h) variations. The observed patterns have been ascribed to different factors, such as temperature-mediated air-surface exchange and variability of planetary boundary layer (PBL) height and dynamics. Here, we present a new modeling approach developed in order to investigate the short-term variability in air concentrations of organic pollutants at a local scale. A new dynamic multimedia box model is supplied by a meteorological preprocessor (AERMET) with hourly values of air compartment height and wind speed. The resulting model is tested against an existing dataset of PCB air concentrations measured in Zurich, Switzerland. Results show the importance of such modeling approach in elucidating the short- and long-term behavior of semivolatile contaminants in the air/soil system.     

Study on the dynamics of algal bloom and its influence factors in Tolo Harbour, Hong Kong.

In this paper, the semi-enclosed bay named Tolo Harbour and Channel in Hong Kong, which was frequently attacked by red tides, was used as a case study. Data sets related to marine water quality, river nutrients, and meteorological conditions recorded between 1988 and 1999 were chosen for statistical analysis. A multivariate analysis showed that algal growth, represented by the chlorophyll a concentration, had obvious spatial and temporal variations in the study area. The chlorophyll a concentration had a consistently decreasing trend from the inner part of the Harbour and surface waters to the outer part and bottom waters. The temporal variations had a markedly seasonal variation with high bioproductivity in spring and winter. There were long-term fluctuations in the chlorophyll a concentration with a high-low-high pattern in the study period. Nutrients and hydrological and meteorological conditions were important factors of algal bloom. Besides nitrogen, which was the most critical factor of algal bloom for the whole water body, total phosphorus in the surface waters and phosphate (PO4) and silica (SiO2) in the bottom waters also showed strongly positive or negative correlations with the chlorophyll a level. For the meteorological conditions, global solar radiation was the key factor of massive algal bloom in the study period, while rainfall and wind direction were the most important factors of seasonal variation.     

Variability of tropospheric hydroperoxides at a coastal surface site in Antarctica

The annual cycles of hydrogen peroxide (H₂O₂) and methylhydroperoxide (MHP) have been investigated at a remote site in Antarctica in order to study seasonal variations as well as chemical processes in the troposphere. The measurements have been performed from March 1997 to January 1998 and in February 1999 at the German Antarctic research station Neumayer which is located at 70°39′S, 8°15′W. The obtained time series for hydrogen peroxide and methylhydroperoxide in near-surface air represents the first all-year measurements in Antarctica and indicates clearly the occurrence of seasonal variations. During polar night mean values of 0.054±0.046 ppbv (range<0.03–0.11 ppbv) for hydrogen peroxide and 0.089±0.052 ppbv (range<0.05–0.14 ppbv) for methylhydroperoxide were detected. At the sunlit period higher Mixing ratios were found, 0.20±0.13 ppbv (range<0.03–0.91 ppbv) for hydrogen peroxide and 0.19±0.10 ppbv (range<0.05–0.89 ppbv) for methylhydroperoxide. Occasional long-range transport of air masses from mid-latitudes caused enhanced peroxide concentrations at polar night. During the period of stratospheric ozone depletion we observed peroxide mixing ratios comparable to typical winter levels.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM₁₀ and PM_2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria.The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ emission factors are in average 5–45% lower during the month 6–10 compared to the annual average.The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km⁻¹ for PM₁₀, 30–60 mg km⁻¹ for PM_2.5 and 20–50 mg km⁻¹ for the exhaust emissions.We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here.Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas.All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM₁₀ emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM₁₀ levels.     

Weekly periodicities of meteorological variables and their possible association with aerosols in Korea

The weekly periodicities in meteorological variables and its association with aerosols in Korea are investigated using long-term surface measurements of meteorology (1975–2005) and aerosols (1999–2005). Through an analysis of the annual (and/or seasonal) values averaged over 10 stations, we identified distinct weekly periodicities in the daily minimum temperature (T_min), diurnal temperature range (DTR), cloud fraction, and solar insolation, although they have different characteristics from each other. The weekly association among variables is discussed in this study. Positive anomalies of the cloud fraction and T_min and negative anomalies of solar insolation and DTR are seen for the second half of the week and the reverse for the first half of the week, i.e., more cloudiness and less insolation for Wednesday?Thursday and less cloudiness and more insolation for Monday?Tuesday. Furthermore, seasonal dependence of weekly anomalies shows that the weekly periodicities are enhanced especially in autumn, more than 2–3 times as great as those of the annual mean. The weekly cycles in such variables are most likely driven by changes in cloud fraction, possibly through aerosol–cloud interactions induced by aerosol variations between working weekdays and Sunday, which are clearly identified in PM10 weekly cycles. This study also suggests that the weekly periodicities in meteorological variables are possibly associated with long-range transport of weekly periodicities, as well as aerosol–cloud-precipitation interactions over the region.     

Variations of surface O3 in August at a rural site near Shanghai: influences from the West Pacific subtropical high and anthropogenic emissions

Large day-to-day variability in O₃ and CO was observed at Chongming, a remote rural site east of Shanghai, in August 2010. High ozone periods (HOPs) that typically lasted for 3?C5?days with daily maximum ozone exceeding 102?ppb were intermittent with low ozone periods (LOPs) with daily maximum ozone less than 20?ppb. The correlation analysis of ozone with meteorological factors suggests that the large variations of surface ozone are driven by meteorological conditions correlated with the changes in the location and intensity of the west Pacific subtropical high (WPSH) associated with the East Asian summer monsoon (EASM). When the center of WPSH with weaker intensity is to the southeast of Chongming site, the mixing ratios and variability of surface ozone are higher. When the center of WPSH with stronger intensity is to the northeast of Chongming site, the mixing ratios and variability of surface ozone are lower. Sensitivity simulations using the GEOS-Chem chemical transport model indicate that meteorological condition associated with WPSH is the primary factor controlling surface ozone at Chongming in August, while local anthropogenic emissions make significant contributions to surface ozone concentrations only during HOP.     

Airborne black carbon concentrations over an urban region in western India—temporal variability, effects of meteorology, and source regions

This study characterizes over 5 years of high time resolution (5 min), airborne black carbon (BC) concentrations (July 2003 to December 2008) measured over Ahmedabad, an urban region in western India. The data were used to obtain different time averages of BC concentrations, and these averages were then used to assess the diurnal, seasonal, and annual variability of BC over the study region. Assessment of diurnal variations revealed a strong association between BC concentrations and vehicular traffic. Peaks in BC concentration were co-incident with the morning (0730 to 0830, LST) and late evening (1930 to 2030, LST) rush hour traffic. Additionally, diurnal variability in BC concentrations during major festivals (Diwali and Dushera during the months of October/November) revealed an increase in BC concentrations due to fireworks displays. Maximum half hourly BC concentrations during the festival days were as high as 79.8 μg m^?3. However, the high concentrations rapidly decayed suggesting that local meteorology during the festive season was favorable for aerosol dispersion. A multiple linear regression (MLR) model with BC as the dependent variable and meteorological parameters as independent variables was fitted. The variability in temperature, humidity, wind speed, and wind direction accounted for about 49% of the variability in measured BC concentrations. Conditional probability function (CPF) analysis was used to identify the geographical location of local source regions contributing to the effective BC measured (at 880 nm) at the receptor site. The east north-east (ENE) direction to the receptor was identified as a major source region. National highway (NH8) and two coal-fired thermal power stations (at Gandhinagar and Sabarmati) were located in the identified direction, suggesting that local traffic and power plant emissions were likely contributors to the measured BC.     

A climatology of Be at four high-altitude stations at the Alps and the Northern Apennines

The ⁷Be activity concentrations measured from 1996 to 1998 at four high-altitude stations, Jungfraujoch—Switzerland, Zugspitze—Germany, Sonnblick—Austria and Mt. Cimone—Italy, were analyzed in combination with a set of, meteorological and atmospheric parameters such as the tropopause height, relative and specific humidity and also in conjunction with 3D back-trajectories in order to investigate the climatological features of ⁷Be. A frequency distribution analysis on ⁷Be activity concentrations revealed the existence of two concentration classes around 1.5 and 6 mBq m⁻³ and a transition class between the two modes of the distribution at 3–4 mBq m⁻³. Cross-correlation analysis performed between ⁷Be and a number of meteorological and atmospheric parameters at the first three stations showed a strong negative correlation with relative humidity (−0.56, −0.51, −0.41) indicating the importance of wet scavenging as a controlling mechanism. Also, the positive correlation with the height of 3-days back-trajectories and tropopause height (+0.49/+0.43, +0.59/+0.36, +0.44/+0.38) shows that downward transport from the upper or middle to lower troposphere within anticyclonic conditions plays also an important role. Trajectory statistics showed that low ⁷Be concentrations typically originate from lower-altitude subtropical ocean areas, while high concentrations arrive from the north and high altitudes, as is characteristic for stratospheric intrusions. Although the ⁷Be activity concentrations are highly episodic, the monthly means indicate an annual cycle with a late-summer maximum at all stations. The correlation coefficients calculated for monthly means of the ⁷Be and atmospheric data suggest that the main predictor controlling the seasonality of the ⁷Be concentrations is tropopause height (+0.76, +0.56, +0.60), reflecting more vertical transport from upper tropospheric levels into the lower troposphere during the warm season than during the cold season.     

Factors Influencing the Variability of SO2 Concentrations in Istanbul

ABSTRACT

The correlation between sulfur dioxide (SO₂) concentrations measured at the European and Asian sides of Istanbul and meteorological parameters is investigated using principal component analysis (PCA) and multiple regression analysis techniques. Several meteorological parameters are selected to represent the atmospheric conditions during two winter periods: 1993–1994 and 1994–1995. Six principal components are found to explain the majority of the observed meteorological variability. Surface pressure, 850-mb temperature, and surface zonal (east-west) and meridional (north-south) winds show high loadings on separate factors identified by PCA. We seek dominant meteorological parameters that control the SO₂ levels at each monitoring station. Several multiple regression analysis models are fitted to the data from each monitoring station using six principal components and previous day SO₂ concentrations as independent variables.

Results suggest that the most important parameters, highly correlated with SO₂ concentrations in the Istanbul metropolitan area, are atmospheric pressure and surface zonal and meridional winds. These components have more influence on the determination of the air pollution levels at the Asian side than at the European side.     

Characterizing ammonia emissions from swine farms in eastern North Carolina: part 1--conventional lagoon and spray technology for waste treatment

Ammonia (NH3) fluxes from waste treatment lagoons and barns at two conventional swine farms in eastern North Carolina were measured. The waste treatment lagoon data were analyzed to elucidate the temporal (seasonal and diurnal) variability and to derive regression relationships between NH3 flux and lagoon temperature, pH and ammonium content of the lagoon, and the most relevant meteorological parameters. NH3 fluxes were measured at various sampling locations on the lagoons by a flowthrough dynamic chamber system interfaced to an environmentally controlled mobile laboratory. Two sets of open-path Fourier transform infrared (FTIR) spectrometers were also used to measure NH3 concentrations for estimating NH3 emissions from the animal housing units (barns) at the lagoon and spray technology (LST) sites. Two different types of ventilation systems were used at the two farms. Moore farm used fan ventilation, and Stokes farm used natural ventilation. The early fall and winter season intensive measurement campaigns were conducted during September 9 to October 11, 2002 (lagoon temperature ranged from 21.2 to 33.6 degrees C) and January 6 to February 2, 2003 (lagoon temperature ranged from 1.7 to 12 degrees C), respectively. Significant differences in seasonal NH3 fluxes from the waste treatment lagoons were found at both farms. Typical diurnal variation of NH3 flux with its maximum value in the afternoon was observed during both experimental periods. Exponentially increasing flux with increasing surface lagoon temperature was observed, and a linear regression relationship between logarithm of NH3 flux and lagoon surface temperature (T1) was obtained. Correlations between lagoon NH3 flux and chemical parameters, such as pH, total Kjeldahl nitrogen (TKN), and total ammoniacal nitrogen (TAN) were found to be statistically insignificant or weak. In addition to lagoon surface temperature, the difference (D) between air temperature and the lagoon surface temperature was also found to influence the NH3 flux, especially when D > 0 (i.e., air hotter than lagoon). This hot-air effect is included in the statistical-observational model obtained in this study, which was used further in the companion study (Part II), to compare the emissions from potential environmental superior technologies to evaluate the effectiveness of each technology.     

Environmental filtering in the dry season and spatial structuring in the wet: different fish community assembly rules revealed in a large subtropical floodplain lake

Although environmental filtering and spatial structuring are commonly regarded as two key factors shaping community dynamics, their relative contribution remains unknown for numerous aquatic ecosystems, particularly highly dynamic floodplain lakes. This issue is here addressed by examining the seasonal metacommunity dynamics of freshwater fishes in Lake Dongting, a large subtropical lake of the middle Chang-Jiang basin in southern China. Physicochemical variables and fish assemblages were recorded at 20 sampling sites during the wet, normal, and dry seasons. Distance-based redundancy analysis and associated variation partitioning were used to examine the relative role of environmental variables and spatial factors in fish community assembly in each season. Analysis results demonstrated that the relative contribution of environmental filtering and spatial structuring varied depending on environmental features and the extent of hydrological connectivity in different seasons. Intensified physicochemical parameters in the dry season convinced the enhanced environmental filtering, whereas high hydrological connectivity in the wet season favored the stronger spatial process. Specifically, the community assembly processes were temporally dynamic; spatial structuring (or mass effects), resulting from excessively high dispersal rates, was dominant during the flooding season, and environmental filtering was stronger than spatial structuring (or dispersal limitation) during the non-flooding season. These findings highlight the importance of conserving local habitats of Lake Dongting during the dry and normal seasons, and maintaining of the flood pulse of the lake and its natural variability during the wet season. Apparently, the construction of a water-level regulation project at the Chenglingji Channel, the outlet watercourse of Lake Dongting, is not supported because it will change the flood pulse of this lake and thus impact habitat heterogeneity or variability.

     

Analysis of air quality data near roadways using a dispersion model

We used a dispersion model to analyze measurements made during a field study conducted by the U.S. EPA in July–August 2006, to estimate the impact of traffic emissions on air quality at distances of tens of meters from an eight-lane highway located in Raleigh, NC. The air quality measurements consisted of long path optical measurements of NO at distances of 7 and 17 m from the edge of the highway. Sonic anemometers were used to measure wind speed and turbulent velocities at 6 and 20 m from the highway. Traffic flow rates were monitored using traffic surveillance cameras. The dispersion model [Venkatram, A., 2004. On estimating emissions through horizontal fluxes. Atmospheric Environment 38, 2439–2446] explained over 60% of the variance of the observed path averaged NO concentrations, and over 90% of the observed concentrations were within a factor of two of the model estimates.Sensitivity tests conducted with the model indicated that the traffic flow rate made the largest contribution to the variance of the observed NO concentrations. The meteorological variable that had the largest impact on the near road NO concentrations was the standard deviation of the vertical velocity fluctuations, σ_w. Wind speed had a relatively minor effect on concentrations. Furthermore, as long as the wind direction was within ±45° from the normal to the road, wind direction had little impact on near road concentrations. The measurements did not allow us to draw conclusions on the impact of traffic-induced turbulence on dispersion. The analysis of air quality and meteorological observations resulted in plausible estimates of on-road emission factors for NO.