This paper provides an initial assessment of Myanmar’s PM0.1 (small to nanoparticulate matter) levels. In Mandalay, the second-largest city in Myanmar, there are two distinct seasons: wet and dry. In 2019, a Nano-sampler with PM10/2.5/1.0/0.5/0.1 stages was used to collect ambient particles. The first was from March, and the second was from December, both in 2019. The PM2.5 and PM10 levels exceeded the World Health Organization Air Quality Guidelines in both periods. The PM0.1 level ranged from 10 to 23 µg/m3, with an average of 15.21 ± 4.85 µg/m3. The Char-EC/Soot-EC ratios in PM0.1 (0.79–1.57) indicate a mixed combustion regime, with a slight dominance of biomass burning. However, values close to unity suggest substantial contributions from fossil fuel combustion, particularly diesel emissions, reflecting a dual-regime aerosol system in the study area. On the other hand, local and transboundary biomass burning emissions affect both fine particles (PM0.5−1.0 and PM1.0−2.5) and coarse particles (PM> 10 and PM2.5−10). In December, emissions from the area may be more important than smoke from biomass burning in other parts of Myanmar. However, long-range transport from South Asia and the west side of Mandalay is important for elevated PM levels during March. This outcome will help Myanmar and other developing nations affected by burning tropical biomass transition to long-term air quality management.
Multimodal large language models (MLLMs), which integrate language and visual cues for problem-solving, are crucial for advancing artificial general intelligence (AGI). However, current benchmarks for measuring the intelligence of MLLMs suffer from limited scale, narrow coverage, and unstructured knowledge, offering only static and undifferentiated evaluations. To bridge this gap, we introduce MDK12-Bench, a large-scale multidisciplinary benchmark built from real-world K–12 exams spanning six disciplines with 141K instances and 6,225 knowledge points organized in a six-layer taxonomy. Covering five question formats with difficulty and year annotations, it enables comprehensive evaluation to capture the extent to which MLLMs perform over four dimensions: 1) difficulty levels, 2) temporal (cross-year) shifts, 3) contextual shifts, and 4) knowledge-driven reasoning. We propose a novel dynamic evaluation framework that introduces unfamiliar visual, textual, and question form shifts to challenge model generalization while improving benchmark objectivity and longevity by mitigating data contamination. We further evaluate knowledge-point reference-augmented generation (KP-RAG) to examine the role of knowledge in reasoning. Key findings reveal limitations in current MLLMs in multiple aspects and provide guidance for enhancing model reasoning, robustness, and AI-assisted education.
Water is a critical resource for data centers and an efficient means of cooling. However, meeting the growing water demand of data centers requires substantial peak water withdrawals, which many communities in the United States cannot supply, especially during the hottest days of the year. This largely overlooked water capacity constraint is emerging as a bottleneck for data centers and can force operators to rely on less efficient dry cooling, further stressing the power grid during summer peaks. In this paper, we focus on the direct water withdrawal of U.S. data centers for cooling and examine their impacts on public water systems. Our analysis indicates that, if the 2024 water use intensity persists, U.S. data centers could collectively require 697-1,451 million gallons per day (MGD) of new water capacity through 2030, comparable to New York City's average daily supply of roughly 1,000 MGD. Under an optimistic scenario with a compound annual water use intensity reduction by 10
Metals in water and sediments of aquatic ecosystems pose significant ecological risks. However, existing methods to integrate and effectively communicate the overall toxicity risks of multiple metals are limited. This study introduces a toxicity factor (Tf) and toxicity index (TI) to comprehensively evaluate and communicate sediment metal pollution and associated risks to biota in a single figure. Furthermore, they are applied to Inle Lake, Myanmar, a crucial component of the Indo-Burma biodiversity hotspot, impacted by untreated sewage, uncontrolled waste disposal, agriculture, and artisanal mining. Sediment metal concentrations (mg/kg) decreased in the order Al (mean: 16,049) > Fe (11,191) > Mn (411) > Cr (34.7) > Zn (33.2) > Pb (22.4) > Ni (14.9) > As (9.69) > Cu (8.17) > Se (1.83). Contamination factor analysis indicated very high pollution by Al (6.99), considerable pollution by Cr (5.26), Pb (4.85), Ni (4.50), Cu (3.64), and Fe (3.09), and moderate pollution by Zn, Se, Mn, and As. The pollution load index (PLI) was highest (7.26) downstream of textile-weaving industries (site S1). Tf analysis identified possible toxicity risks for As, Cr, Ni, and Pb at points S1 and S5, while TI values (S1: 0.99; S5: 0.90) suggested these points were close to the threshold of possible toxicity. Acid-extractable fractions according to BCR-701 protocol revealed lower bioavailability of Cr despite its elevated total concentration, suggesting a primarily lithogenic source. Metal uptake by water hyacinth exhibited no direct correlation with sediment levels, emphasizing variability in metal bioavailability within the lake ecosystem.
In this study, the activated carbon derives from plum seed shells are used for the treatment of textile wastewater collected from the Wandwin area which has high concentrations of parameters such as pH, colour, TSS, TDS, COD, and BOD. pH value of textile wastewater is 12.4 that leads to alkaline, so pH adjusts with alum coagulants. After treatment the COD concentration can be reduced from 1280 mg/l to 20 mg/l by using equilibrium concentration of activated carbon, Ce =50 mg/. The adsorption data followed the Langmuir isotherm (R² = 0. 0.97832). Other parameters also significantly decreased from 74.74 % to 98.29%. According to the National Enviromental Quality Guideline (NEQG) Myanmar, all treated water parameters comply with NEQG standards. Therefore, the plum seed shell activated carbon is an effective and sustainable material for the treatment of textile wastewater and treated effluent can be safely discharged into the surrounding water bodies.
