Feasibility of coupling a thermal/optical carbon analyzer to a quadrupole mass spectrometer for enhanced PM2.5 speciation

A thermal/optical carbon analyzer (TOA), normally used for quantification of organic carbon (OC) and elemental carbon (EC) in PM_2.5 (fine particulate matter) speciation networks, was adapted to direct thermally evolved gases to an electron impact quadrupole mass spectrometer (QMS), creating a TOA-QMS. This approach produces spectra similar to those obtained by the Aerodyne aerosol mass spectrometer (AMS), but the ratios of the mass to charge (m/z) signals differ and must be remeasured using laboratory-generated standards. Linear relationships are found between TOA-QMS signals and ammonium (NH₄⁺), nitrate (NO₃^?), and sulfate (SO₄^2-) standards. For ambient samples, however, positive deviations are found for SO₄^2-, compensated by negative deviations for NO₃^?, at higher concentrations. This indicates the utility of mixed-compound standards for calibration or separate calibration curves for low and high ion concentrations. The sum of the QMS signals across all m/z after removal of the NH₄⁺, NO₃^?, and SO₄^2- signals was highly correlated with the carbon content of oxalic acid (C?H?O?) standards. For ambient samples, the OC derived from the TOA-QMS method was the same as the OC derived from the standard IMPROVE_A TOA method. This method has the potential to reduce complexity and costs for speciation networks, especially for highly polluted urban areas such as those in Asia and Africa.

Implications: Ammonium, nitrate, and sulfate can be quantified by the same thermal evolution analysis applied to organic and elemental carbon. This holds the potential to replace multiple parallel filter samples and separate laboratory analyses with a single filter and a single analysis to account for a large portion of the PM_2.5 mass concentration.     

Real<Emphasis Type="Bold">-</Emphasis>time water quality monitoring using Internet of Things in SCADA

Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.     

Seasonal and diurnal measurements of carbon monoxide and nonmethane hydrocarbons at Mt. Wilson,California: Indirect evidence of atomic Cl in the Los Angeles basin

We present a study of the seasonal and diurnal variability of carbon monoxide and selected volatile organic compounds in the Los Angeles area. Measurements were made during four different nine-day field campaigns in April/May, September, and November, 2007, and February, 2008, at the Mt. Wilson sampling site, which is located at an elevation of approximately 1700 m in the San Gabriel Mountains overlooking Pasadena and the Los Angeles basin. The results were used to characterize the Mt. Wilson site as a representative location for monitoring integrated Los Angeles basin emissions, and, by reference to carbon monoxide emissions, to estimate average annual emissions. The considerable seasonal variability of many hydrocarbons, in both their measured mixing ratios and their relationship to carbon monoxide, was indicative of variable source strengths. Most interestingly, perturbation of C₄ hydrocarbon ratios suggested an enhanced role for chlorine chemistry during the month of September, likely as the result of Los Angeles’ coastal location. Such coastal influence was confirmed by observations of enhanced mixing ratios of marine halocarbons, as well as air mass back trajectories.     

Trace metals in soil and leaves of Jacaranda mimosifolia in Tshwane area,South Africa

Plant and soil have been identified as major sink of pollutants in the environment. We evaluated the reliability of biomonitoring of heavy metals in Tshwane area with the use of leaves of Jacaranda mimosifolia. The concentrations of heavy metals such as Ca, Mg, Fe, Pb, Zn, Cu, Sb were measured in leaves of J. mimosifolia and soils collected from 10 sites in the city of Tshwane during two sampling periods. The metals were analyzed with the use of ICP-MS. The result shows significant differences in the concentration of trace metals in all the sites (p < 0.01). The differences between the two sampling periods were statistically significant (p < 0.01). Concentration of metals from high traffic and industrial sites were significantly higher than in the residential areas (p < 0.01). Concentration factor suggests that translocation of metals from roots to leaves could be relevant only for some metals such as Ca, Mg and Sb. The study reveals an anthropogenic source for the trace metals. Leaves of J. mimosifolia were found to be a useful biomonitor of the determined trace metals.     

Urban tracer dispersion experiments during the second DAPPLE field campaign in London 2004

As part of the DAPPLE programme two large scale urban tracer experiments using multiple simultaneous releases of cyclic perfluoroalkanes from fixed location point sources was performed. The receptor concentrations along with relevant meteorological parameters measured are compared with a three screening dispersion models in order to best predict the decay of pollution sources with respect to distance. It is shown here that the simple dispersion models tested here can provide a reasonable upper bound estimate of the maximum concentrations measured with an empirical model derived from field observations and wind tunnel studies providing the best estimate. An indoor receptor was also used to assess indoor concentrations and their pertinence to commonly used evacuation procedures.     

A 3D aerosol and visibility information system for urban areas using remote sensing and GIS

Currently, the depiction of urban air quality at boundary layer scale uses modelled climatic and land cover data. However, such models are difficult to verify, and only low to moderate accuracy may be achieved due to the complexity of the input data required and the reliance on assumptions about dispersion patterns. The provision of comprehensive air quality data to urban residents in city districts, at a level of detail commensurate with other Location-Based Services (LBS) which are time- and place-sensitive, has therefore not been possible. A method for urban air quality monitoring over cities at boundary layer scale, other than by the use of air quality models is presented here. The system presented uses empirical Aerosol Optical Thickness (AOT) data in near-real time, combining AOT data from AERONET with aerosol vertical profiles computed from twice-daily MODIS satellite images at 500 m resolution, to give three dimensional (3D) air quality data over the urban landscape. There has been no previous attempt to project the horizontal spatial distribution of aerosols from satellite image pixels into a vertical dimension to give a spatially comprehensive three dimensional record of air quality. The paper describes the sources and accuracy of the AOT data input to the system as well as its storage and retrieval on a Geographic Information System (GIS) platform, to provide air quality and visibility information according to user query at any 3D geographical location, including individual buildings or building floor.     

The seasonal cycles and photochemistry of C2–C5 alkanes at Mace Head

Continuous in-situ measurements of NMHCs at Mace Head, Ireland during two full annual cycles from January 2005 to January 2007 were used to investigate NMHC emission sources and transport including dilution and photochemical oxidation. The Mace Head research station is ideally located to sample a wide range of air masses including polluted European transport, clean North Atlantic and Arctic air masses and the ultra-clean, Southern Atlantic air masses. The variety in air mass sampling is used to investigate interaction of emissions, transport, dilution and photochemistry. Variability of long-lived hydrocarbon ratios is used to assess and estimate typical transport times from emission source to the Mace Head receptor. Seasonality in the ratios of isomeric alkane pairs (for butane and pentanes) are used to assess the effects of atmospheric transport and photochemical ageing. Finally, the natural logarithms of NMHC ratios are used to assess photochemical oxidation.     

Comprehensive analysis of the carbon impacts of vehicle intelligent speed control

In recent years sophisticated technologies have been developed to control vehicle speed based on the type of road the vehicle is driven on using Global Positioning Systems and in-car technology that can alter the speed of the vehicle. While reducing the speed of road vehicles is primarily of interest from a safety perspective, vehicle speed is also an important determinant of vehicle emissions and thus these technologies can be expected to have impacts on a range of exhaust emissions. This work analyses the results from a very large, comprehensive field trial that used 20 instrumented vehicles with and without speed control driven almost 500,000 km measuring vehicle speed at 10 Hz. We develop individual vehicle modal emissions models for CO₂ for 30 Euro III and Euro IV cars at a 1-Hz time resolution. Generalized Additive Models were used to describe how emissions from individual vehicles vary depending on their driving conditions, taking account of variable interactions and time-lag effects. We quantify the impact that vehicle speed control has on-vehicle emissions of CO₂ by road type, fuel type and driver behaviour. Savings in CO₂ of ≈6% were found on average for motorway-type roads when mandatory speed control was used compared with base case conditions. For most other types of road, speed control has very little effect on emissions of CO₂ and in some cases can result in increased emissions for low-speed limit urban roads. We also find that there is on average a 20% difference in CO₂ emission between the lowest and highest emitting driver, which highlights the importance of driver behaviour in general as a means of reducing emissions of CO₂.     

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Simulating chemistry–aerosol–cloud–radiation–climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem)

The chemistry–aerosol–cloud–radiation–climate feedbacks are simulated using WRF/Chem over the continental U.S. in January and July 2001. Aerosols can reduce incoming solar radiation by up to ?9% in January and ?16% in July and 2-m temperatures by up to 0.16 °C in January and 0.37 °C in July over most of the continental U.S. The NO₂ photolysis rates decrease in July by up to ?8% over the central and eastern U.S. where aerosol concentrations are high but increase by up to 7% over the western U.S. in July and up to 13% over the entire domain in January. Planetary boundary layer (PBL) height reduces by up to ?23% in January and ?24% in July. Temperatures and wind speeds in July in big cities such as Atlanta and New York City reduce at/near surface but increase at higher altitudes. The changes in PBL height, temperatures, and wind speed indicate a more stable atmospheric stability of the PBL and further exacerbate air pollution over areas where air pollution is already severe. Aerosols can increase cloud optical depths in big cities in July, and can lead to 500–5000 cm^?3 cloud condensation nuclei (CCN) at a supersaturation of 1% over most land areas and 10–500 cm^?3 CCN over ocean in both months with higher values over most areas in July than in January, particularly in the eastern U.S. The total column cloud droplet number concentrations are up to 4.9 × 10⁶ cm^?2 in January and up to 11.8 × 10⁶ cm^?2 in July, with higher values over regions with high CCN concentrations and sufficient cloud coverage. Aerosols can reduce daily precipitation by up to 1.1 mm day^?1 in January and 19.4 mm day^?1 in July thus the wet removal rates over most of the land areas due to the formation of small CCNs, but they can increase precipitation over regions with the formation of large/giant CCN. These results indicate potential importance of the aerosol feedbacks and an urgent need for their accurate representations in current atmospheric models to reduce uncertainties associated with climate change predictions.