Concentrations and source apportionment of PM10 and associated elemental and ionic species in a lignite-burning power generation area of southern Greece

The seasonal variation, characteristics and secondary generation of PM2.5 in Xi'an, China, especially during pollution events

As an important central city in western China, Xi'an has the worst atmospheric pollution record in China and many measures have been taken to improve the air quality in the past few years. In this study, PM_2.5 samples were collected across four seasons from 2017 to 2018 in Xi'an. Organic carbon and elemental carbon, water soluble ions, and elements were monitored to assess the air quality. The average annual PM_2.5 concentration was (134.9 ± 48.1 μg/m³), with the highest concentration in winter (188.8 ± 93.2 μg/m³), and lowest concentration in summer (71.2 ± 12.1 μg/m³). The secondary generation of sulfate (SO₄^2-) and nitrate (NO₃^-) was strong in spring, and secondary organic carbon (SOC) was formed in all seasons. The compositions of PM_2.5 changed greatly during a sandstorm occurred and the Spring Festival. The sandstorm played a positive role in removing local pollutant NO₃^-, but also increased the concentration of SO₄^2-, however both the concentration of SO₄^2- and NO₃^- greatly increased by secondary generation during Spring Festival. Potential source analysis showed that during the sandstorm, pollutants were transported over a long distance from the northwest of China, whereas it was mainly from the local and surrounded emissions during the Spring Festival. Except Ca²⁺ and geological dust (GM), the other components in PM_2.5 increased significantly on the day of the Spring Festival. During sampling time in Xi'an, the positive matrix factorization (PMF) model analysis showed that PM_2.5 mainly came from vehicle emission, coal combustion, and biomass burning.

Proximity to coal-fired power plants and neurobehavioral symptoms in children

BACKGROUND

Coal-fired power plants are a major source of air pollution that can impact children's health. Limited research has explored if proximity to coal-fired power plants contributes to children's neurobehavioral disorders.

OBJECTIVE

This community-based study collected primary data to investigate the relationships of residential proximity to power plants and neurobehavioral problems in children.

METHODS

235 participants aged 6-14 years who lived within 10 miles of two power plants were recruited. Exposure to particulate matter ≤10 μm (PM₁₀) was measured in children's homes using personal modular impactors. Neurobehavioral symptoms were assessed using the Child Behavior Checklist (CBCL). Multiple regression models were performed to test the hypothesized associations between proximity/exposure and neurobehavioral symptoms. Geospatial statistical methods were used to map the spatial patterns of exposure and neurobehavioral symptoms.

RESULTS

A small proportion of the variations of neurobehavioral problems (social problems, affective problems, and anxiety problems) were explained by the regression models in which distance to power plants, traffic proximity, and neighborhood poverty was statistically associated with the neurobehavioral health outcomes. Statistically significant hot spots of participants who had elevated levels of attention deficit hyperactivity disorder, anxiety, and social problems were observed in the vicinity of the two power plants.

SIGNIFICANCE

Results of this study suggest an adverse impact of proximity to power plants on children's neurobehavioral health. Although coal-fired power plants are being phased out in the US, health concern about exposure from coal ash storage facilities remains. Furthermore, other countries in the world are increasing coal use and generating millions of tons of pollutants and coal ash. Findings from this study can inform public health policies to reduce children's risk of neurobehavioral symptoms in relation to proximity to power plants.

Nanoparticle Emission and Characterization from Pre-Dried Lignite and Bituminous Coal Co-Combustion

Nowadays, the high share of electricity production from renewables drives coal-fired power plants to adopt a more flexible operation scheme and, at the same time, maintain flue gas emissions within respective standards. A 500 kWth pulverized coal furnace was used to study pre-dried lignite combustion or co-combustion as an available option for these plants. Bituminous coal from Czech Republic and pre-dried lignite from Greece were blended for the experiments. Particle emissions measurements with a heated Electrical Low Pressure Impactor (ELPI+) and Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM/EDS) analyses were performed. The effect of the pre-dried lignite proportions in the fuel feed and the combustion conditions regarding the combustion air staging were the two parameters selected for this study. Skeletal density values were measured from the cyclone prior to the impactor. Results are depicted with respect to the aerodynamic and Stokes diameter for impactor stages. The presence of pre-dried lignite in the fuel blend lowers the particle matter (PM) PM2.5, PM1 and PM0.1 emissions, thus having a positive impact on ESP’s fractional and overall efficiency. The staged combustion air feed reduces the particle emissions in all cases. Sulfur content follows a pattern of higher concentration values for finer particles.

Chemical characterization and receptor modeling of PM10 in the surroundings of the opencast lignite mines of Western Macedonia, Greece

The Western Macedonian Lignite Center (WMLC) in northwestern Greece is the major lignite center in the Balkans feeding four major power plants of total power exceeding 4 GW. Concentrations of PM₁₀ (i.e., particulate matters with diameters ≤10 μm) are the main concern in the region, and the high levels observed are often attributed to the activities related to power generation. In this study, the contribution of fugitive dust emissions from the opencast lignite mines to the ambient levels of PM₁₀ in the surroundings was estimated by performing chemical mass balance (CMB) receptor modeling. For this purpose, PM₁₀ samples were concurrently collected at four receptor sites located in the periphery of the mine area during the cold and the warm periods of the year (November-December 2011 and August-September 2012), and analyzed for a total of 26 macro- and trace elements and ionic species (sulfate, nitrate, chloride). The robotic chemical mass balance (RCMB) model was employed for source identification/apportionment of PM₁₀ at each receptor site using as inputs the ambient concentrations and the chemical profiles of various sources including the major mine operations, the fly ash escaping the electrostatic filters of the power plants, and other primary and secondary sources. Mean measured PM₁₀ concentrations at the different sites ranged from 38 to 72 μg m^-3. The estimated total contribution of mines ranged between 9 and 22% in the cold period increasing to 36-42% in the dry warm period. Other significant sources were vehicular traffic, biomass burning, and secondary sulfate and nitrate aerosol. These results imply that more efficient measures to prevent and suppress fugitive dust emissions from the mines are needed.

Source apportionment of PM10 and PM2.5 in major urban Greek agglomerations using a hybrid source-receptor modeling process

A hybrid source-receptor modeling process was assembled, to apportion and infer source locations of PM₁₀ and PM_2.5 in three heavily-impacted urban areas of Greece, during the warm period of 2011, and the cold period of 2012. The assembled process involved application of an advanced computational procedure, the so-called Robotic Chemical Mass Balance (RCMB) model. Source locations were inferred using two well-established probability functions: (a) the Conditional Probability Function (CPF), to correlate the output of RCMB with local wind directional data, and (b) the Potential Source Contribution Function (PSCF), to correlate the output of RCMB with 72h air-mass back-trajectories, arriving at the receptor sites, during sampling. Regarding CPF, a higher-level conditional probability function was defined as well, from the common locus of CPF sectors derived for neighboring receptor sites. With respect to PSCF, a non-parametric bootstrapping method was applied to discriminate the statistically significant values. RCMB modeling showed that resuspended dust is actually one of the main barriers for attaining the European Union (EU) limit values in Mediterranean urban agglomerations, where the drier climate favors build-up. The shift in the energy mix of Greece (caused by the economic recession) was also evidenced, since biomass burning was found to contribute more significantly to the sampling sites belonging to the coldest climatic zone, particularly during the cold period. The CPF analysis showed that short-range transport of anthropogenic emissions from urban traffic to urban background sites was very likely to have occurred, within all the examined urban agglomerations. The PSCF analysis confirmed that long-range transport of primary and/or secondary aerosols may indeed be possible, even from distances over 1000km away from study areas.

Assessment of PM2.5 sources and their corresponding level of uncertainty in a coastal urban area using EPA PMF 5.0 enhanced diagnostics

Datasets that include only the PM elemental composition and no other important constituents such as ions and OC, should be treated carefully when used for source apportionment. This work is demonstrating how a source apportionment study utilizing PMF 5.0 enhanced diagnostic tools can achieve an improved solution with documented levels of uncertainty for such a dataset. The uncertainty of the solution is rarely reported in source apportionment studies or it is reported partially. Reporting the uncertainty of the solution is very important especially in the case of small datasets. PM2.5 samples collected in Patras during the year 2011 were used. The concentrations of 22 elements (Z=11-33) were determined using PIXE. Source apportionment analysis revealed that PM2.5 emission sources were biomass burning (11%), sea salt (8%), shipping emissions (10%), vehicle emissions (33%), mineral dust (2%) and secondary sulfates (33%) while unaccounted mass was 3%. Although Patras city center is located in a very close proximity to the city's harbor, the contribution of shipping originating emissions was never before quantified. As rotational stability is hard to be achieved when a small dataset is used the rotational stability of the solution was thoroughly evaluated. A number of constraints were applied to the solution in order to reduce rotational ambiguity.