The distributions and biogeochemical cycles of arsenic in the aquatic environment have captured the interest of geochemists due to arsenic's multiple chemical forms, the toxicity of certain arsenic species and large anthropogenic input. Seasonal variations in the dissolved inorganic arsenic concentration and speciation in Jiaozhou Bay, which is located on the west coast of the Yellow Sea in northern China, are presented here. Three cruises were carried out in Jiaozhou Bay under varying tidal regimes, one at neap tide and one at spring tide in August and one at spring tide in October of 2001. In addition to the transect surveys, the main sources of dissolved inorganic arsenate and arsenite in Jiaozhou Bay, including riverine input from five major tributary rivers, atmospheric dry and wet depositions, and groundwater and wastewater input, were collected in different seasons to estimate arsenic transport through different sources. The mean concentrations of total dissolved inorganic arsenic (TDIAs, As (V+III)) in Jiaozhou Bay were statistically comparable between summer and autumn, with higher concentrations at the northwest and northeast parts of the bay, reflecting human activities. The As (III)/TDIAs ratio ranged between 0.045 and 0.68, with an average of 0.16, implying that arsenate was the dominating species in Jiaozhou Bay. A preliminary box model was established to estimate the water-mass balance and arsenic budgets for Jiaozhou Bay, which demonstrated that river inputs and atmospheric depositions were the main sources of arsenic into Jiaozhou Bay. The concentrations of dissolved inorganic arsenic in Jiaozhou Bay have decreased in the last two decades. Compared with other areas in the world, the concentration of arsenic in Jiaozhou Bay remains at the natural level and this region can be characterized as a less disturbed area.

Three cruises were carried out in Jiaozhou Bay (JZB) in the neap tide in October 2002 (fall) and in both neap and spring tides in May 2003 (spring) to understand the relative importance of external nutrient inputs versus physical transport and internal biogeochemical processes. Nutrients ([graphic removed] , [graphic removed] , [graphic removed] , [graphic removed] , silicic acid, total dissolved nitrogen (TDN) and phosphorus (TDP), dissolved organic nitrogen (DON) and phosphorus (DOP)) were measured. The concentrations of nutrients were higher in the northern part than in the southern part. High concentrations of [graphic removed] and DON in JZB demonstrated the anthropogenic input. Ambient nutrient ratios indicated that the potential limiting nutrients for phytoplankton growth were silicon, and then phosphorus. Nutrients showed an obvious tidal effect with low values at flood tide and high values at ebb tide. Nutrient elements were transported into JZB in the north and output in the south (i.e., into the Yellow Sea), which varied with season, tidal cycle and investigation sites. Water exchange between JZB and the Yellow Sea exports [graphic removed] , [graphic removed] and DON out of JZB, while it inputs [graphic removed] , silicic acid and DOP into JZB. Nutrient budgets demonstrate that riverine input and wastewater discharge are major sources of nutrients, while residual flow is of minor importance in JZB ecosystem. JZB is a sink for the nutrient elements we studied except for DON. Stoichiometric calculations demonstrate that JZB is a net autotrophic system.

Within the European monitoring network (EMEP, http://www.emep.int) several different sampling procedures for measuring the main air components have been applied. This has contributed to systematic concentration differences and a comparability problem. Since 1997 co-located experiments in 15 countries have been carried out to quantify these differences. In addition, three major measurement campaigns were organized by EMEP between 1985 and 1991. Differences among results depend on the concentration level and methods used. The decrease in SO₂ concentrations over the last twenty years has placed greater demands on the methodology. Absorbing solutions methods for SO₂, (H₂O₂ and tetrachloromercurate (TCM)) typically have higher detection limits than the reference method, which uses KOH impregnated filters. The TCM method also has problems with negative interference, especially in summertime. UV fluorescence monitors have in a few cases proven to give good results, but interferences, detection limit and poor maintenance can be problems. For NO₂, many countries are using the TGS absorption solution method, which has a higher detection limit than the reference method using NaI impregnated glass sinters. The Salzmann method gives unreliable results at concentrations below 1 μgN/m³, and even at higher concentrations the uncertainty is rather unsatisfactory. The chemiluminescence monitor with molybdenum converters tends to systematically overestimate NO₂ concentrations, possibly because zero-drift problems and the non-specific response to NO₂. Particulate sulphate measurements in general have lower bias and uncertainties than gas and other aerosol measurements.

Studies on N losses from ornamental plantings - other than turf - are scant despite the ubiquity of these landscaping elements. We compared pore water NO₃ and extractable soil NO₃ and NH₄ in areas with turf, areas with seven different types of ornamental landscape plantings, and a native woodland. Turf areas received annual N inputs of ~48 kg ha-¹ and annual flowers received ~24 kg N ha-¹ at the time of planting. None of the other areas were fertilized during the course of the study. Data were collected on 23 occasions between June 2002 and November 2003. Pore water NO₃ concentrations at a 60-cm depth - based on pooled data - were highest (1.4 to 7.8 mg NO₃-N l-¹) under ground covers, unplanted-mulched areas, turf, deciduous trees, and evergreen trees, with no differences among these vegetation types. Lower values were observed under woodlands, annual and perennial flowers, and evergreen and deciduous shrubs. Pore water NO₃ concentrations exceeded the drinking water regulatory limit of 10 mg NO₃-N l-¹ under ground covers, turf and unplanted-mulched areas in 39, 20 and 10% of samples, respectively. Leaching losses of NO₃-N over 18 months ranged from 0.17 kg N ha-¹ in the woodlands to 34.97 kg N ha-¹ under ground covers. Annual NO₃ losses under unplanted-mulched areas and ground covers were approximately twice the average N input (10 kg N ha-¹ year-¹) from atmospheric deposition. Extractable NO₃ in woodland soils (0.5 μg NO₃-N g-¹) was lower than for all other vegetation types (3.1-7.8 μg NO₃-N g-¹). Extractable NH₄ levels were highest in woodlands, deciduous trees, and annual flowers (6.7-10.1 μg NH₄-N g-¹). Most vegetation types appear to act as net N sinks relative to atmospheric inputs, whereas unplanted-mulched areas and areas planted with ground covers act as net sources of NO₃ to groundwater.

The atmospheric deposition of reactive nitrogen on turf grassland in Tsukuba, central Japan, was investigated from July 2003 to December 2004. The target components were ammonium, nitrate, and nitrite ions for wet deposition and gaseous ammonia, nitric and nitrous acids, and particulate ammonium, nitrate, and nitrite for dry deposition. Organic nitrogen was also evaluated by subtracting the amount of inorganic nitrogen from total nitrogen. A wet-only sampler and filter holders were used to collect precipitation and the atmospheric components, respectively. An inferential method was applied to calculate the dry deposition velocity of gases and particles, which involved the effects of surface wetness and ammonia volatilization through stomata on the dry deposition velocity. The mean fraction of the monthly wet to total deposition was different among chemical species; 37, 77, and 1% for ammoniacal, nitrate-, and nitrite-nitrogen, respectively. The annual deposition of inorganic nitrogen in 2004 was 47 and 48 mmol m-² yr-¹ for wet and dry deposition, respectively; 51% of atmospheric deposition was contributed by dry deposition. The annual wet deposition in 2004 was 20, 27, and 0.07 mmol m-² yr-¹, and the annual dry deposition in 2004 was 35, 7.4, and 5.4 mmol m-² yr-¹ for ammoniacal, nitrate-, and nitrite-nitrogen, respectively. Ammoniacal nitrogen was the most important reactive nitrogen because of its remarkable contribution to both wet and dry deposition. The median ratio of the organic nitrogen concentration to total nitrogen was 9.8, 17, and 15% for precipitation, gases, and particles, respectively.

Numerous studies have shown that Steamboat Creek in Nevada is highly contaminated with mercury, with aqueous mercury concentrations more than two orders of magnitude greater than nearby mountain streams. One objective of this study was to determine if the new Spreadsheet-based Ecological Risk Assessment for the Fate of Mercury (SERAFM) model could be calibrated to the concentrations of unfiltered and dissolved total mercury, and unfiltered and dissolved MeHg in the water column for a reach on SBC and a related constructed wetland mesocosm for different seasons and residence times. SERAFM is a new U.S. Environmental Protection Agency steady state, single segment, mass balance mercury model that has been applied to lakes, and this study also examined the model’s applicability for modeling an arid flowing water environment in different seasons. The average combined error between observed and model-estimated mercury concentrations was 12% and 17% for the reach and mesocosm, respectively. Some recommendations are proposed that may allow SERAFM to better model flowing systems.

The quantification of arsenic biovolatilization by microscopic filamentous fungi Aspergillus clavatus, A. niger, Trichoderma viride and Penicillium glabrum under laboratory conditions is discussed in this article. The fungi were cultivated on a liquid medium enriched with inorganic arsenic in pentavalent form (H₃AsO₄). Filamentous fungi volatilized 0.010 mg to 0.067 mg and 0.093 mg to 0.262 mg of arsenic from cultivation systems enriched with 0.25 mg (5 mg.l-¹ of arsenic in culture media) and 1.00 mg of arsenic (20 mg.l-¹ of arsenic in culture media), respectively. These results represent the loss of arsenic after a 30-day cultivation from cultivation systems. The production of volatile arsenic derivatives by the A. niger and A. clavatus strains was also determined by hourly sorption using the sorbent Anasorb (CSC) on the 29th day of cultivation.

A comparison was made of the ability of three different methods to describe the deposition and distribution of chloride from deicing salt in the roadside environment along a highway: direct sampling of airborne deposition (including snow ploughing) in containers; soil sampling and analysis of chloride content in the topsoil; and direct current resistivity measurements. Each method showed a distribution with significant decreasing values with increasing distance from the road. Two transport mechanisms, splash and spray, were identified when describing the airborne deposition. A mathematical model that includes these two transport mechanisms was adopted, and the total amount of airborne deposition on the ground 0–100 m from the road was estimated to approximately 45% of the salt applied on the road. The main part of the chloride spread by air and ploughing ended up within 10 m from the road. The soil sampling and resistivity measurements also showed the highest impact within this distance. The variation in chloride content in the soils reflected a poorer drainage ability of fine-grained soils compared to more coarse-grained soils. The resistivity measurements represented an integrated value of the differences in geology, water content and salinity. The increase in resistivity with distance from road in the topsoil was interpreted to reflect the distribution of chloride from deicing salt.

Multi-block (heavy metals, pesticides, physico-chemical parameters) data set pertaining to the soils of alluvium region in Indo-Gangetic plains was analyzed using principal component analysis (PCA) and multiple factor analysis (MFA) methods to delineate the contaminated sites and to identify the possible contamination sources in the study region. In normal PCA, the first three factors were dominated mainly by heavy metals, pesticides and physico-chemical variables, respectively, thus identifying samples/sites contaminated with these. The MFA results, due to its unique weighting scheme of variables of different blocks extracted, to more realistic information about the spatial distribution of samples and relationships among the variables. MFA minimized the influence of variables of one single block on the first few components, allowing variables of all blocks equally to share the common MFA space. This resulted in delineating the sites/regions contaminated with variables (Al, Co, Cu, Mn, Ni, Pb, V, Na, SO₄, aldrin, lindane, HCB, HCH, DDT, and endosulfan) of all the blocks, rather than by particular block variables as in case of normal PCA, where, the variables of single block dominate the first factors, suppressing other block variables. MFA which can be considered as a method for standardization of the multi-block variables was successfully applied to the three block data set of soils.

We attempted to elucidate seasonal variations in fossil-fuel-derived carbon (FC) and biomass-derived carbon (BC) in urban atmospheric aerosols. We undertook continuous measurements of the composition of fine particle (PM₂.₁) in central Tokyo, including the ¹⁴C/¹²C ratio. The percent modern carbon (pMC) contained in all samples averaged 43, and the highest was 54 in late December and the lowest was 31 in early August. From the observed carbonaceous component concentrations and the pMC we could calculate the content ratio of FC and BC in PM₂.₁ and investigate their seasonal variations. Although there was almost no seasonal variation in the ratio of FC, the ratio of BC was observed to rise in early winter. This indicates that FC is influenced by the emission sources without seasonal variations (such as automobiles driven in urban areas). Furthermore, there is significant correlation between BC and organic carbon (OC), and even for urban areas, it is considered that the contribution of biomass carbon to OC in PM₂.₁ is high.