Visibility and Incidence of Respiratory Diseases During the 1998 Haze Episode in Brunei Darussalam

— Air pollution episodes as a result of forest fires in Brunei Darussalam and neighbouring regions have reached hazardous levels in recent years. Such episodes are generally associated with poor visibility and air quality conditions. In the present study, data on PM₁₀ (particulate matter of size less than 10 microns) and CO in Brunei Darussalam have been considered to study the incidence of respiratory diseases whereas data on relative humidity (RH) in addition to PM₁₀ have been used to explain the visibility with a particular emphasis on haze episode during 1998.¶Initial exploratory analysis indicates significant correlation of visibility with PM₁₀ and RH. An attempt has been made to explain visibility on the basis of PM₁₀ and RH using multiple linear regression analysis. The regression model shows that PM₁₀ and RH are two significant factors affecting the visibility at a given site. Further, canonical correlation, a multivariate method of analysis, has been used to explain the incidence of respiratory diseases as a function of air quality during the haze period. The results indicate that PM₁₀ and CO levels during the haze period have a significant bearing on the incidence of respiratory diseases (Asthma, Acute Respiratory Infections and Influenza (ARII)).     

Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China

In January 2013,a long-lasting episode of severe haze occurred in central and eastern China,and it attracted attention from all sectors of society.The process and evolution of haze pollution episodes were observed by the"Forming Mechanism and Control Strategies of Haze in China"group using an intensive aerosol and trace gases campaign that simultaneously obtained data at 11 ground-based observing sites in the CARE-China network.The characteristics and formation mechanism of haze pollution episodes were discussed.Five haze pollution episodes were identified in the Beijing-Tianjin-Hebei(Jing-Jin-Ji)area;the two most severe episodes occurred during 9–15 January and 25–31 January.During these two haze pollution episodes,the maximum hourly PM2.5mass concentrations in Beijing were 680 and 530μg m 3,respectively.The process and evolution of haze pollution episodes in other major cities in the Jing-Jin-Ji area,such as Shijiazhuang and Tianjin were almost the same as those observed in Beijing.The external cause of the severe haze episodes was the unusual atmospheric circulation,the depression of strong cold air activities and the very unfavorable dispersion due to geographical and meteorological conditions.However,the internal cause was the quick secondary transformation of primary gaseous pollutants to secondary aerosols,which contributed to the"explosive growth"and"sustained growth"of PM2.5.Particularly,the abnormally high amount of nitric oxide(NOx)in the haze episodes,produced by fossil fuel combustion and vehicle emissions,played a direct or indirect role in the quick secondary transformation of coal-burning sulphur dioxide(SO2)to sulphate aerosols.Furthermore,gaseous pollutants were transformed into secondary aerosols through heterogeneous reactions on the surface of fine particles,which can change the particle’s size and chemical composition.Consequently,the proportion of secondary inorganic ions,such as sulphate and nitrate,gradually increased,which enhances particle hygroscopicity and thereby accelerating formation of the haze pollution.     

Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control

The Nested Air Quality Prediction Model System(NAQPMS)was used to investigate the temporal and spatial variations of PM2.5over tropospheric central eastern China in January 2013.The impact of regional transport and its implications on pollution prevention and control were also examined.Comparison between simulated and observed PM2.5showed NAQPMS was able to reproduce the evolution of PM2.5during heavy haze episodes.The results indicated that regional transport of PM2.5played an important role in regional haze episodes in the city cluster including Hebei,Beijing and Tianjin(HBT).The cross-city clusters transport outside HBT and transport among cities inside HBT contributed 20%–35%and 26%–35%of PM2.5as compared with local emission,in HBT respectively.To meet the Air Quality Standards for Grade II,90%,90%and65%of emissions would have to be cut down in Hebei,Tianjin and Beijing,if non-control strategy was taken in the surrounding city clusters of HBT.This implicated that control of emissions in one city cluster is not sufficient to reduce regional haze events,and joint efforts among city clusters are essential.Besides regional transports,two-way feedback between boundary-layer evolution and PM2.5also significantly contributed to the formation of heavy hazes,which contributed 30%of monthly average PM2.5concentration in HBT.     

Chemistry of Forest Fires and Regional Haze with Emphasis on Southeast Asia

— The current state of knowledge regarding the chemistry of forest fires and regional haze is reviewed. More than 100 compounds have been identified in wood smoke and many of these have also been observed in field studies. Products of biomass combustion can have different environmental effects: CO₂ and CH₄ may contribute to global warming, NO_x and SO₂ could contribute to rainwater acidity, whereas smoke particles and polynuclear aromatic hydrocarbons (PAHs) could affect human health. Also, photochemical reactions of primary emissions from biomass fires can lead to the production of secondary pollutants such as O₃. Regional haze episodes caused by forest fires have occurred in SE Asia on several occasions during the 1990s and the reported studies of these episodes are reviewed. Only total suspended particles (TSP) were determined in the earlier studies, and more comprehensive chemical investigations have only emerged during the more recent episodes, notably those of 1997 and 1998. To date, most of the measurements have centred on criteria pollutants (SO₂, NO₂, CO, O₃ and PM₁₀), however, other pollutants (e.g., VOCs, PAHs) have also been determined in certain studies. Rainwater analyses suggest that forest fires do not have a major acidifying effect because dissolved acidic gases (e.g., SO₂) are neutralised by alkaline substances (e.g., Ca, Mg, K) that are also emitted by forest fires. There is a need for further laboratory and field studies in order to investigate important pollutant transformation mechanisms.     

Estimation of the PM2.5 effective hygroscopic parameter and water content based on particle chemical composition: Methodology and case study

Particle hygroscopicity plays a key role in understanding the mechanisms of haze formation and particle optical properties. The present study developed a method for predicting the effective hygroscopic parameter k and the water content of PM_(2.5) on the basis of the k-K?hler theory and bulk chemical components of PM_(2.5). Our study demonstrated that the effective hygroscopic parameter can be estimated using the PM_(2.5) mass concentration, water-soluble ions, and total water-soluble carbon. By combining the estimated k and ambient relative humidity, the water content of PM_(2.5) can be further estimated. As an example, the k and water content of PM_(2.5) in Beijing were estimated utilizing the method proposed in this study. The annual average value of k of PM_(2.5) in Beijing was 0.25±0.09, the maximum k value 0.26±0.08 appeared in summer, and the seasonal variation is insignificant. The PM_(2.5) water content was determined by both the PM_(2.5) hygroscopicity and the ambient relative humidity(RH). The annual average mass ratio of water content and PM_(2.5) was 0.18±0.20, and the maximum value 0.31±0.25 appeared in summer. Based on the estimated water content of PM_(2.5) in Beijing, the relationship between the PM_(2.5) water content and RH was parameterized as: m(%)=0.03+(5.73×10~(-8)) ×RH~(3.72).This parametric formula helps to characterize the relationship between the PM_(2.5) mass concentration and atmospheric visibility.     

Application of DARLAM to Regional Haze Modeling

— The CSIRO Division of Atmospheric Research limited area model (DARLAM) is applied to atmospheric transport modeling of haze in southeast Asia. The 1998 haze episode is simulated using an emission inventory derived from hotspot information and adopting removal processes based on SO₂.¶Results show that the model is able to simulate the transport of haze in the region. The model images closely resemble the plumes of NASA Total Ozone Mapping Spectrometer and Meteorological Service Singapore haze maps. Despite the limitation of input data, particularly for haze emissions, the three-month average pattern correlation obtained for the whole episode is 0.61. The model has also been able to reproduce the general features of transboundary air pollution over a long period of time. Predicted total particulate matter concentration also agrees reasonably well with observation.¶The difference in the model results from the satellite images may be attributed to the large uncertainties of emission, simplification of haze deposition and transformation mechanisms and the relatively coarse horizontal and vertical resolution adopted for this particular simulation.     

The Airshed for Ozone and Fine Particulate Pollution in the Eastern United States

— To examine the spatial scales associated with atmospheric pollutants such as ozone (O₃) and fine particulate matter (PM_2.5), we employ the following five techniques: (1) Analysis of the persistence of high O₃ concentrations aloft; (2) spatial and lag correlations between the short-term components (i.e., weather-induced variations) in the time series of O₃ and PM_2.5 throughout the eastern United States; (3) analysis of mixed-layer forward trajectories compiled at different locations on a climatological basis to identify the potential region covered in 1-day of atmospheric transport; (4) analysis of three-dimensional Lagrangian trajectories of tracer particles for three high-O₃ episode events in the summer of 1995; and (5) analysis of the spatial extent over which emissions have an impact through photochemical model simulations. Regardless of the method chosen, the results demonstrate that pollutants such as O₃ and PM_2.5 have the potential to affect regions having spatial scales of several hundred kilometers. This finding has implications to regulatory policies for addressing the pollution problem, and for optimally designing monitoring networks for such pollutants.     

Bayesian autoregressive spatiotemporal model of PM<Subscript>10</Subscript> concentrations across Peninsular Malaysia

Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM₁₀ are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM₁₀ levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM₁₀ concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM₁₀ in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM₁₀ concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM₁₀ forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R² and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.     

Average mass concentrations of TSP, PM10 and PM2.5 in Erzurum urban atmosphere, Turkey

In this study, particulate matters (TSP, PM₁₀, PM_2.5 and PM_10–2.5) which are hazardous for environment and human health were investigated in Erzurum urban atmosphere at a sampling point from February 2005 to February 2006. During sampling, two low volume samplers were used and each sampling period lasted approximately 24 h. In order for detection of representative sampling region and point of Erzurum, Kriging method was applied to the black smoke concentration data for winter seasons. Mass concentrations of TSP, PM₁₀ and PM_2.5 of Erzurum urban atmosphere were measured on average, as 129, 31 and 13 μg/m³, respectively, in the sampling period. Meteorological factors, such as temperature, wind speed, wind direction and rainfall were typically found to be affecting PMs, especially PM_2.5. Air temperature did not seem to be significantly affecting TSP and PM₁₀ mass concentrations, but had a considerably negative induction on PM_2.5 mass concentrations. However, combustion sourced PM_2.5 was usually diluted from the urban atmosphere by the speed of wind, soil sourced coarse mode particle concentrations (TSP, PM₁₀) were slightly affected by the speed of wind. Rainfall was found to be decreasing concentrations to 48% in all fractions (TSP, PM₁₀, PM_10–2.5, PM_2.5) and played an important role on dilution of the atmosphere. Fine mode fraction of PM (PM_2.5) showed significant daily and seasonal variations on mass concentrations. On the other hand, coarse mode fractions (TSP, PM₁₀ and PM_10–2.5) revealed more steady variations. It was observed that fine mode fraction variations were affected by the heating in residences during winter seasons.     

Inter-comparison of seasonal variability and nonlinear trend between AERONET aerosol optical depth and PM10 mass concentrations in Hong Kong

Here we used Empirical Mode Decomposition(EMD) method to study seasonal variability and nonlinear trend of corrected AERONET Aerosol Optical Depth(AOD/Hi) and corrected PM10 mass concentrations(PM10×f(RH)) in Hong Kong during 2005–2011. AOD/Hi is highly correlated with PM10×f(RH) in semi-annual and annual time scales(with correlation coefficient 0.67 for semi-annual and 0.79 for annual components, 95% confidence interval). On the semi-annual scale, both AOD/Hi and PM10×f(RH) can capture the two maxima in March and October, respectively, with much stronger amplitude in March probably due to the long-range transport of dust storm. On the annual cycle, the AOD/Hi and PM10×f(RH), which are negatively correlated with the precipitation and solar radiation, vary coherently with the maxima in February. This annual peak occurs about one month earlier than the first peak of the semi-annual variability in March, but with only half amplitude. During 2005–2011, both AOD/Hi and PM10×f(RH) exhibit the pronounced decreasing trend with the mean rate of 14 μg m–3 per year for PM10×f(RH), which reflects the significant effects of the air pollution control policy in Hong Kong during the past decade. The nonlinear trend analysis indicates that the decreasing of PM10×f(RH) is slower than that of AOD/Hi when the AOD/Hi is less than 0.44 but becomes faster when the AOD/Hi exceeds 0.44. These results illustrate that the AERONET AOD can be used quantitatively to estimate local air-quality variability on the semi-annual, annual, and long-term trend time scales.     

Predicting spatio-temporal concentrations of PM<Subscript>2.5</Subscript> using land use and meteorological data in Yangtze River Delta,China

The prediction of PM_2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM_2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM_2.5 concentrations. We validated the estimated effects by using the cross-validated R² and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM_2.5 concentration, with a reasonably large R² of 0.86 and a small RMSE of 8.15 (μg/m³). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM_2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.     

Fine Particles (PM2.5) in Residential Areas of Lucknow City and Factors Influencing the Concentration

Urban populations are exposed to a high level of fine and ultrafine particles from motor vehicle emissions which affect human health. To assess the hourly variation of fine particle (PM_2.5) concentration and the influence of temperature and relative humidity (RH) on the ambient air of Lucknow city, monitoring of PM_2.5 along with temperature and RH was carried out at two residential locations, namely Vikas Nagar and Alambagh, during November 2005. The 24 h mean PM_2.5 concentration at Alambagh was 131.74 μg/m³ and showed an increase of 13.74%, which was significantly higher (p < 0.05) than the Vikas Nagar level. The 24 h mean PM_2.5 on weekdays for both locations was found to be 142.74 μg/m³ (an increase of 66.23%) which was significantly higher (p < 0.01) than the weekend value, indicating that vehicular pollution is one of the important sources of PM_2.5. The mean PM_2.5 at night for all the monitoring days was 157.69 μg/m³ and was significantly higher (p < 0.01) than the daytime concentration (89.87 μg/m³). Correlation and multiple regressions showed that the independent variables, i. e., time, temperature, and RH together accounted for 54%, whereas RH alone accounted for 53% of total variations of PM_2.5, suggesting that RH is the best influencing variable to predict the PM_2.5 concentration in the urban area of Lucknow city. The 24 h mean PM_2.5 for all the monitoring days was found to be higher than the NAAQS recommended by the US‐EPA (65 μg/m³) and can be considered to be an alarming indicator of adverse health effects for city dwellers.     

Three-dimensional analysis of ozone and PM<Subscript>2.5</Subscript> distributions obtained by observations of tethered balloon and unmanned aerial vehicle in Shanghai,China

A lightweight unmanned aerial vehicle (UAV) and a tethered balloon platform were jointly used to investigate three-dimensional distributions of ozone and PM_2.5 concentrations within the lower troposphere (1000 m) at a localized coastal area in Shanghai, China. Eight tethered balloon soundings and three UAV flights were conducted on May 25, 2016. Generalized additive models (GAMs) were used to quantitatively describe the relationships between air pollutants and other obtained parameters. Field observations showed that large variations were captured both in the vertical and horizontal distributions of ozone and PM_2.5 concentrations. Significant stratified layers of ozone and PM_2.5 concentrations as well as wind directions were observed throughout the day. Estimated bulk Richardson numbers indicate that the vertical mixing of air masses within the lower troposphere were heavily suppressed throughout the day, leading to much higher concentrations of ozone and PM_2.5 in the planetary boundary layer (PBL). The NO and NO₂ concentrations in the experimental field were much lower than that in the urban area of Shanghai and demonstrated totally different vertical distribution patterns from that of ozone and PM_2.5. This indicates that aged air masses of different sources were transported to the experimental field at different heights. Results derived from the GAMs showed that the aggregate impact of the selected variables for the vertical variations can explain 94.3% of the variance in ozone and 94.5% in PM_2.5. Air temperature, relative humidity and atmospheric pressure had the strongest effects on the variations of ozone and PM_2.5. As for the horizontal variations, the GAMs can explain 56.3% of the variance in ozone and 57.6% in PM_2.5. The strongest effect on ozone was related to air temperature, while PM_2.5 was related to relative humidity. The output of GAMs also implied that fine aerosol particles were in the stage of growth in the experimental field, which is different from ozone (aged air parcels of ozone). Geographical parameters influenced the horizontal variations of ozone and PM_2.5 concentrations by changing underlying surface types. The differences of thermodynamic properties between land and sea resulted in quick changes of PBL height, air temperature and dew point over the coastal area, which was linked to the extent of vertical mixing at different locations. The results of GAMs can be used to analyze the sources and formation mechanisms of ozone and PM_2.5 pollutions at a localized area.     

Source apportionment and secondary organic aerosol estimation of PM2.5 in an urban atmosphere in China

PM2.5 is the key pollutant in atmospheric pollution in China.With new national air quality standards taking effect,PM2.5 has become a major issue for future pollution control.To effectively prevent and control PM2.5,its emission sources must be precisely and thoroughly understood.However,there are few publications reporting comprehensive and systematic results of PM2.5 source apportionment in the country.Based on PM2.5 sampling during 2009 in Shenzhen and follow-up investigation,positive matrix factorization(PMF)analysis has been carried out to understand the major sources and their temporal and spatial variations.The results show that in urban Shenzhen(University Town site),annual mean PM2.5 concentration was 42.2μg m?3,with secondary sulfate,vehicular emission,biomass burning and secondary nitrate as major sources;these contributed30.0%,26.9%,9.8%and 9.3%to total PM2.5,respectively.Other sources included high chloride,heavy oil combustion,sea salt,dust and the metallurgical industry,with contributions between 2%–4%.Spatiotemporal variations of various sources show that vehicular emission was mainly a local source,whereas secondary sulfate and biomass burning were mostly regional.Secondary nitrate had both local and regional sources.Identification of secondary organic aerosol(SOA)has always been difficult in aerosol source apportionment.In this study,the PMF model and organic carbon/elemental carbon(OC/EC)ratio method were combined to estimate SOA in PM2.5.The results show that in urban Shenzhen,annual SOA mass concentration was 7.5μg m?3,accounting for 57%of total organic matter,with precursors emitted from vehicles as the major source.This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in China.     

Lockdown as an Empirical Strategy to Curb Air Pollution: Evidence from a Quasi-Natural Experiment

In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID-19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM₁₀ (27%), PM_2.5 (19%), NO₂ (74%), SO₂ (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21-days lockdown is implemented, then a reduction of 42 µg m⁻³ in PM₁₀, 23 µg m⁻³ in PM_2.5, 14 µg m⁻³ in NO₂, 2 µg m⁻³ in SO₂, and 0.7 mg m⁻³ in CO can be achieved.     

Temporal dynamics,meteorological effects,secondary organic aerosol estimation,and source identification of size-segregated carbonaceous aerosols

During the period 2019–2020, size-segregated aerosol samples containing elemental and organic carbon (EC and OC) were investigated. These samples were collected weekly using an eight-stage cascade impactor from an urban site located at Aksaray University, Aksaray. The quantification of EC and OC was carried out through a thermal-optical transmission device. The results revealed consistent size distribution attributes of EC and OC between winter and summer. Although EC accounted for an insignificant percentage (4.4%) of particulate matter (PM) in the PM_9.0–10.0 fraction during winter, a more substantial portion of OC in the same fraction (13.4%) comprised EC. Seasonal variations were distinct for EC but not significant for OC. Strong correlations between OC and EC were observed in coarse particle fractions, indicating a common source, with weaker correlations in fine particles. The highest OC/EC ratio was in the PM_0.43–0.65 fraction, followed by PM_2.1–3.3. The ratio of OC to EC in fine PM exceeded the threshold of 15 consistently. The observation indicates that as particle size increases, there is a noticeable decline in the OC to EC ratios. Secondary organic aerosols (SOA) accounted for 60.8% (winter) and 89.8% (summer) of OC values, emphasizing the substantial impact of SOA on Aksaray's atmosphere. Both seasons exhibited a multimodal distribution of ambient OC. In winter, the EC distribution was dominated by fine particles, with a bimodal pattern (PM_1.1–2.1 and PM_0.43–0.65 peaks). Common pollutant sources, including traffic emissions, road dust, biogenic emissions, and coal combustion, were identified for both seasons in coarse and fine particle fractions. These findings underscore the importance of emission control strategies targeting fine PM in Aksaray.     

Geographies of uncertainty in the health benefits of air quality improvements

Assessing the long-term benefits of marginal improvements in air quality from regulatory intervention is methodologically challenging. In this study, we explore how the relative risks (RRs) of mortality from air pollution exposure change over time and whether patterns in the RRs can be attributed to air quality improvements. We employed two-stage multilevel Cox models to describe the association between air pollution and mortality for 51 cities with data from the American Cancer Society (ACS) cohort (N = 264,299, deaths = 69,819). New pollution data were computed through models that predict yearly average fine particle (PM_2.5) concentrations throughout the follow-up (1982–2000). Average PM_2.5 concentrations from 1999 to 2000 and sulfate concentrations from 1980 were also examined. We estimated the RRs of mortality associated with air pollution separately for five time periods (1982–1986, 1987–1990, 1991–1994, 1995–1998, and 1999–2000). Mobility models were implemented with a sub-sample of 100,557 subjects to assist with interpreting the RR estimates. Sulfate RRs exhibit a large decline from the 1980s to the 1990s. In contrast, PM_2.5 RRs follow the opposite pattern, with larger RRs later in the 1990s. The reduction in sulfate RR may have resulted from air quality improvements that occurred through the 1980s and 1990s in response to the acid rain control program. PM_2.5 concentrations also declined in many places, but toxic mobile sources are now the largest contributors to PM in urban areas. This may account for the heightened RR of mortality associated with PM_2.5 in the 1990s. The paper concludes with a three alternative explanations for the temporal pattern of RRs, each emphasizing the uncertainty in ascribing health benefits to air quality improvements.     

Correlation between plasmid DNA damage induced by PM10 and trace metals in inhalable particulate matters in Beijing air

Based on the study of Beijing PM₁₀ bioreactivity with the newly developed plasmid DNA assay method, and analysis for trace elements of PM₁₀, the cause of plasmid DNA damage by PM₁₀ was investigated. The study showed that plasmid DNA oxidative damages by PM₁₀ are of difference in different seasons at various areas. The concentrations of TM50 of PM₁₀ in whole samples respectively collected at urban and comparison sites during winter were 900 μg mL⁻¹ and 74 μg mL⁻¹, while those in their corresponding soluble fractions were 540 μg mL⁻¹ and 86 μg mL⁻¹. In contrast, TM50 contents of PM₁₀ from summer whole samples at urban areas and comparison sites were 116 μg mL⁻¹ and 210 μg mL⁻¹, whereas those in their soluble fractions were 180 μg mL⁻¹ and 306 μg mL⁻¹. The difference of bioreactivity of Beijing PM₁₀ resulted from the variation of trace elements. The oxidative damage of plasmid DNA caused by Pb, Zn, As in PM₁₀ (whole sample) was relatively strong. TM50 and Mn, V, Zn display stronger correlation in the soluble fraction. It implies that Zn could be the major trace element in Beijing PM₁₀ which contributes to oxidative damage to plasmid DNA.     

Atmospheric Particulate Concentration Measured in an Urban Area Bandung

— Atmospheric particulate concentration for total suspended particles (TSP) and for PM₁₀ (particulate matter under 10 micron) was measured in Jalan Braga and ITB campus, Bandung. Six samples were collected over one- or two-day time periods using High Volume Sampler (HVS) for TSP and Low Volume Sampler (LVS) or Anderson Cascade Impactor for PM₁₀. Samples were further analyzed to determine concentrations of metals, sulfate and nitrate. Concentration of NO_x (NO and NO₂) was also measured hourly and simultaneously during the sampling period. The results from this study show that the atmospheric particulate concentration in Jalan Braga for TSP ranged from 304.04 to 363.17, and for PM₁₀ concentration ranged from 277.02 to 336.44 μg/m³. The lead concentrations were 1.42–2.37 μg/m³ in the TSP and 0.81–1.57 μg/m³ in the PM₁₀. The nitrate concentrations were 5.89–6.51 μg/m³ and 2.27–3.45 μg/m³ for the TSP and PM₁₀, respectively. The hourly NO_x concentration varied between 0.14–0.35 ppm. The total elements (metals, sulfate and nitrate) found in the samples contribute from 20 to 25% of the total particulate concentration.     

Transport and dispersal of organic debris (peat blocks) in upland fluvial systems

This paper assesses the mechanisms and pathways by which peat blocks are eroded and transported in upland fluvial systems. Observations and experiments from the north Pennines (UK) have been carried out on two contrasting river systems. Mapping of peat block distributions and appraisal of reach‐based sediment budgets clearly demonstrates that macro‐size peat is an important stream load component. In small streams block sizes can approximate the channel width and much of the peat is transported overbank. Local ‘peat jams’ and associated mineral deposition may provide an important component of channel storage. In larger systems peat blocks rapidly move down‐channel and undergo frequent exchanges between bed and bank storage. Results of peat block tracing using painted blocks indicate that once submerged, blocks of all sizes are easily transported and blocks break down rapidly by abrasion. Vegetation and bars play an important role in trapping mobile peat. In smaller streams large block transport is limited by channel jams. Smaller blocks are transported overbank but exhibit little evidence of downstream fining. In larger rivers peat blocks are more actively sorted and show downstream reduction in size from source. A simple model relating peat block diameter (D_p) to average flow depth (d) suggests three limiting transport conditions: flotation (D_p < d), rolling (d < D_p > d/2) and deposition (D_p > d/2). Experiments demonstrate that peat block transport occurs largely by rolling and floating and the transport mechanism is probably controlled by relative flow depth (d/D_p ratio). Transport velocity varies with transport mechanism (rolling is the slowest mode) and transport lengths increase as flow depth increases. Abrasion rates vary with the transport mechanism. Rolling produces greater abrasion rates and more rounded blocks. Abrasion rates vary from 0 to 10 g m^?1 for blocks ranging in mass from 10 to 6000 g. Copyright © 2001 John Wiley & Sons, Ltd.