A popular dollar store chain is shutting down hundreds of stores across the country after a rodent infestation.

According to KCRG, Family Dollar stores shut down over 400 shops across the country after the Food and Drug Administration reported potentially contaminated products from “unsanitary conditions, including a rodent infestation.” Yikes. The good news is that it appears that no Iowa locations were impacted. The FDA alerted the public that “products purchased from Jan. 1, 2021, through the present from Family Dollar stores in Alabama, Arkansas, Louisiana, Mississippi, Missouri and Tennessee” may be unsafe for people to use.

Why are Family Dollar products being recalled?

When you look at the FDA findings it’s clear just how unsafe those products could be. KCRG reports that the FDA found unsanitary conditions that included “live rodents, dead rodents… rodent feces and urine, evidence of gnawing, nesting and rodent odors throughout the facility, dead birds and bird droppings.” Grossed out yet? Well, you will be when you hear that 2,300 rodents were found facility between late March and mid-September of last year, according to the business’ internal records. Or how about when they fumigated the place last month and recovered over 1,000 dead rodents.

What Family Dollar products are being recalled?

Food (human and animal) cosmetics, and over-the-counter medicine were all listed as potentially being contaminated with Salmonella. Those infected can often experience fever, diarrhea, nausea, vomiting, abdominal pain, and sometimes serious infections. The products to look out for were those stored in the infested distribution center and shipped to the 404 store locations from January 1, 2021, to the present. You can see a list of products and stores affected HERE.

You are advised to throw out any product that may be affected and contact your physician if you believe you are experiencing health issues related to the recall. For questions regarding the recall, contact Family Dollar Customer Service at 844-636-7687 between 8 a.m. and 4 p.m. CST.

 

KEEP READING: See 25 natural ways to boost your immune system

 

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3.1 Chamber conditions, ventilation, and aerosol characterization

Across all experiments, the mean chamber temperature was 24.1 ± 1.1°C with a relative humidity of 26.0% ± 2.4%. The temperature change during all experiments rose 0.4°C ± 0.2°C and the relative humidity change was 0.2°C ± 0.2°C. Particle clearance by the ventilation system followed first-order exponential decays, with overall clearance rates 74.1% ± 4.4% of decay rates estimated by anemometer readings (Range: 73.1%–76.7%; Figure 2A). Particle decay rates throughout the chamber, as measured by the five OPCs, were largely homogeneous (Figure S1). The experimental decay rates after single coughs were 76.1% ± 1.5% of theoretical values (Range: 74.4%–77.3%). These experimental decay rate magnitudes and variances were comparable to those obtained from particle decay testing, which suggests that the ventilation system promoted adequate air mixing to disperse cough aerosols through the chamber volume. Therefore, we presume similar air mixing within the chamber during ventilation studies for the other two modalities tested.

Ventilation and aerosol characterization. (A) Environmental chamber particle decay rates across HEPA ventilation settings. Dashed lines indicate the theoretical decay rate for each examined ventilation rate; solid lines indicate effective rates determined. Enumerated effective air exchange rates shown at the end time-point with percent error from theoretical (negative value indicates lower than theoretical). (B) Mean chamber mass concentration-time curves of simulated very fine respiratory droplets and aerosol particles for the examined respiratory actions and ventilation rates. (C) Bin-specific particle distributions as determined by mass (bars) and by number of particles (line). The median particle diameter (Dp) indicates the bin. Results are the arithmetic mean ± standard deviation of three independent experiments. Error bars for the number of particles (line) too small to visualize. ACH, Air changes per hour

Chamber aerosol concentrations during simulated respiratory events are shown in Figure 2B. A single simulated cough produced an immediate aerosol influx within the chamber followed by mixing and log-linear decay, except for no ventilation in which the aerosol concentration reached a plateau. As expected, the continuous influx of aerosol during breathing resulted in an initial phase of rapid increase in aerosol particles which decelerated over time, suggesting a steady state between the source aerosol influx and removal by ventilation would be reached over longer testing durations. The bin-specific size and mass distributions of the KCl aerosol averaged over the test duration were similar across modalities. The particle size data indicated that 41.2%–44.4% of the aerosol mass was in the 2–3 μm range (2.5 μm channel) which tapered to a nadir between 4.1% and 4.4% among the 0.65–0.8 μm size range (0.725 μm channel). The remaining three smallest bins each registered between 7.1% and 8.4% of the mass distribution (Figure 2C). On a particle number basis, most particles were detected within the smallest size channel with <3% attributed to the largest size bin. Similar to the proportional mass distribution, particle count size distribution was analogous across the tested modalities and was similar the OPCs within the chamber (Figure S2). The proportional size distribution demonstrated a peak at 0.35 μm—the smallest measured size bin—via OPC and was similar to tests measuring human respiratory aerosols via OPC-based measurements. Of note, the aerosol concentrations measured in the current investigation were 1–2 orders of magnitude higher than concentrations generated by typical human respiratory events.23, 35-37

3.2 Masking, physical distance, and ventilation

The time-concentration curves at the 1.8 m physical distance are shown in Figure 3A; analogous results for the 0.9 m physical distance are presented in Figure S3. For a single cough, aerosol concentrations decayed log-linearly after aerosol generation, while aerosol concentrations during breathing continually increased over time. Donning a 3-ply cotton mask blunted the height of the time-concentration curves. The mean aerosol mass concentrations at the mouth of the breathing recipient over the testing duration are presented in Figure 3B. Overall, universal masking reduced particle exposure compared to unmasked conditions, while exposure reduction by distance and ventilation did not produce discernable patterns of exposure modulation. To determine the relative exposure reduction of universal masking, physical distancing, and ventilation during the 15-min tests, mean mass concentrations were regressed using ordinary least squares (OLS) multiple linear regression. Inclusion of the interaction term did not improve the model for either cough (p = 0.3062) or breathing (p = 0.6475) compared to models without interaction while also increasing the model Akaiki’s Information Criterion, thus, fixed effects OLS models without interaction were constructed. Results of multiple OLS regression are presented in Table 2. Interactions between masking and the other parameters were neither expected nor tested.

Aerosol mass exposure of the recipient. (A) Mass exposure concentration over time across the matrix of modalities, masking status, and ventilation for the 1.8 m physical distance. Results for the 0.9 m physical distance are provided in the Figure S3. Data are the arithmetic mean of three independent experiments. (B) Mean mass exposure over the 15-min simulation period derived from the time curves. Results are the arithmetic mean ± standard deviation of three independent experiments. No statistical comparisons were made between individual groups. ACH, Air changes per hour

TABLE 2.
Regression coefficients for 15- and 60-min tests

Modality	Parameter	Regression coefficients		Percent reduction		t-Value	Pr > |t|	Model adjusted R2
		β	95% CI	Estimate (%)	95% CI (%)
Cough	Constant	3.146	3.010 to 3.282	–	–	46.587	<0.001	0.969
	Distance: 1.8 m	−0.167	−0.294 to −0.040	15.4	3.9 to 25.5	−2.656	0.011
	ACH	−0.044	−0.059 to −0.030	4.3	2.9 to 5.7	−6.125	<0.001
	Masking: Yes	−2.392	−2.519 to −2.265	90.9	89.6 to 91.9	−38.042	<0.001
Breathing	Constant	3.203	3.034 to 3.373	–	–	38.129	<0.001	0.904
	Distance: 1.8 m	−0.060	−0.217 to 0.098	5.8	−10.3 to 19.5	−0.761	0.451
	ACH	−0.006	−0.024 to 0.012	0.6	−1.2 to 2.4	−0.645	0.522
	Masking: Yes	−1.650	−1.808 to −1.492	80.8	77.5 to 83.6	−21.091	<0.001
Breathing 60 mina	Constant	4.569	4.438 to 4.699	–	–	74.588	<0.001	0.976
	ACH	−0.098	−0.112 to −0.083	9.0	7.7 to 10.3	−14.105	<0.001
	Masking: Yes	−1.553	−1.700 to −1.406	76.0	72.8 to 78.8	−22.477	<0.001

• Abbreviation: ACH, Air changes per hour.

Adjusting for ACH and physical distance, universal masking significantly reduced aerosol exposure compared to unmasked exposures (p < 0.001 among all modalities) during the 15-min tests. Fit factors of the 3-ply cloth mask were 4.1 ± 2.6 (n = 43) for the recipient and 1.7 ± 0.6 (n = 42) for the source simulator. The largest reduction in aerosol mass exposure was observed after a single cough (90.9%; 95% CI: 89.6%–91.9%), while exposure reduction was comparatively lower during breathing (80.8%; 95% CI: 77.5%–83.6%). The reduction in mass concentration was likely due to preferential filtration of aerosols >1 µm in diameter by the 3-ply cotton masks fitted to the source and recipient simulators (Figure 4). The differences in exposure reduction among the aerosol generation modalities were likely due to specific changes in aerosol spatiotemporal dispersion when the source was masked. Aerosol plumes generated during both breathing modalities and a single cough escape through face seal leaks.38 The plumes would then be deflected behind and/or to the side of the source and thus effectively farther from the recipient compared with the experiments with no masks. Without chamber mixing, as observed with no ventilation, the cough aerosol deflected by the mask took longer to disperse throughout the chamber compared to without a mask as was observed in Figure 3A. The time-concentration curves for breathing shifted to the right when masked, though not as much as after a single cough, showing that dispersion kinetics likely played a larger role in the heterogeneity observed for exposure reduction among the respiratory actions simulated here. While we cannot rule out the possibility that differential filtration of the source’s mask was influenced by aerosol generation (for example, the higher expulsion velocity during coughing causing greater mask aerosol filtration compared to breathing), our previous work suggests the aerosol generation modality likely does not influence mask collection efficiency for this 3-ply cotton mask (51.7% ± 7.1% for coughing and 44.3% ± 14.0% for breathing).15 Lastly, since the current investigation utilized static breathing simulators, the results do not account for the potential contribution of anthropogenic movement and individual behavior to aerosol particle exposure.39 We have previously observed the presence of the exhalation from a breathing receiver can influence aerosol particle exposure within the experimental configuration contained in this work.12 In a broader context, personal exposure within the indoor environment can be influenced by several factors, such as heterogenous regions of aerosol concentration and changes in air flow patterns by individual bodily movements.40, 41 Therefore, while outside of the scope of the current investigation, anthropogenic factors should be considered especially when contextualizing the efficacy of examined exposure reduction mitigation strategies.

Aerosol size distribution shifts during masking. (A) Bin-specific percent of mass distribution averaged across all ventilation rates. (B) Bin-specific percent change of aerosol distribution stratified by ventilation rate. The median particle diameter (Dp) indicates the bin. Data are the arithmetic mean of three independent experiments. ACH, Air changes per hour

The exposure reductions associated with the other predictor variables varied depending on the respiratory action for the 15-min tests. When controlling for masking, increasing physical distance from 0.9 to 1.8 m significantly reduced aerosol exposure from a single cough by 15.4% (95% CI: 3.9%–25.5%; p = 0.011); increasing ventilation also reduced exposure by 4.3% per ACH (95% CI: 2.9%–5.7%; p < 0.001). Neither increasing ACH (p = 0.522) nor increasing physical distance (p = 0.451) provided protection during breathing for the 15-min tests. When extending the test duration to 60 min for breathing, the mean mass concentration from aerosol generation reached a dynamic equilibrium with each of the examined ACH rates (Figure 5). Analysis for the 60-min tests by OLS regression demonstrated increasing ventilation significantly decreased mean mass concentration by 9.0% (95% CI: 7.7%–10.3%; p < 0.001; Table 2), while universal masking expectedly reduced mean mass concentration significantly. When condensing the total aerosol generation period to the initial 3 min in the short-term aerosol generation tests, increasing ACH became a significant predictor in exposure reduction (5.2%; 95% CI: 3.8%–6.5%; p < 0.001; Table S2). The time-concentration curves of the short-term aerosol generation tests demonstrated the log-linear decay similarly to time-concentration profiles observed from a single cough, albeit shifted to the right to reflect the longer aerosol generation period (Figure S4). This result demonstrates that attainment of a dynamic equilibrium with continuous aerosol input or removal of aerosols produced by an intense, short-term generation event through increasing ventilation can result in significant exposure reduction for a recipient. We did not examine the extended exposure duration for a single cough over 60 min, though we expect increasing ventilation will remain a significant predictor of mean mass concentration reduction.

Extended breathing assessment. Mass exposure concentration-time curves over 60 min for breathing at the 1.8 m physical distance and three ventilation rates. Results are the arithmetic mean ± standard deviation of three independent experiments. ACH, Air changes per hour

With respect to ventilation, the restricted 15-min exposure duration contributed to the lack of pronounced effect of increasing ACH for breathing and can be explained when considering air flow. Ventilation not only provides contaminant removal but also impacts the overall air flow. Modeling of aerosol dispersion through central ventilation systems demonstrates this complex interplay between ventilatory clearance and overall air flow patterns that can, under certain situations, increase the short-term exposure during rapid, thorough mixing42 that was observed during the breathing respiratory action. Increasing ventilation reduced monotonically the bulk aerosol concentration throughout the entire chamber over the total duration of the ventilation testing (Figure 3A) but tended to decrease the time of aerosol contact at the mouth of the recipient. Using a hand-held fog machine, smoke released at the position of the source’s mouth tended to disperse initially, albeit slowly, in the direction of OPCs 2 and 3 (S2 and S3 as designated in Figure 1, respectively) under no ventilation and 4 ACH. With increasing ventilation, the direction of initial smoke dispersion shifted toward the recipient, likely due to the pressure drop produced from the ventilation system supply stream above and behind the recipient (Figure S5). The supply from 4 ACH traveled along the wall with the supply vent and deflected along the front wall prior to dissipation without a discernable air flow pattern. Increasing ventilation to 6 ACH lengthened the travel distance along the front wall to include deflection along the intake wall as well as downwards toward the floor and in the direction of the Back Wall. Under 12 ACH, the air flow pattern followed that of 6 ACH, except the observed air flow along the Front Wall showed more diffuse downwards toward the floor and in the direction of the Back Wall; patterns along the Back Wall were not readily discernable at any ventilation tested. Examination of the OPC-stratified mean mass concentration-time series during breathing without masks and both respiratory actions during universal masking (Figures S6–S9) confirmed the qualitative air flow pattern suggested by the examination using the fog machine, suggesting air currents by the ventilation system in the environmental chamber was a significant contributor to dispersion during comparatively low-velocity aerosol particle release, such as during breathing and around face seal leaks for both respiratory actions. The initial high-velocity aerosol particles released during a cough without masking was comparatively unaffected by air currents described (Figure S8), while post-cough dispersion and mixing were influenced by air flow produced by the ventilation. Therefore, the air currents induced by the ventilation supply tended to influence recipient aerosol exposure where, in extreme cases, increasing ventilation paradoxically increased the receiver mean mass exposure (Figure 3B); this observation was largely independent of physical distance. Physical distance did contribute to aerosol dispersion with a physical distance of 0.9 m under no ventilation and without masking, where the exhalation from the receiver simulator likely increased the lateral dispersion aerosol particles from the source exhalation as reflected by the rapid increases in mean mass concentration measured by OPC sampler S4 (Figure S6) which was otherwise absent when positioned 1.8 m.

The authors opine such increases were caused by the observed thorough air mixing and changes in air flow patterning of aerosols after exhalation from the source, as was noted in particle decay studies and qualitative fog machine testing, in conjunction with the short exposure duration of 15 min. As previously noted, the effect of ventilation was appreciated during the 60-min breathing test and the short-term aerosol generation tests. These results demonstrate that the aerosol reduction measures by ventilation must consider the air mixing, aerosol spatial dispersion, and exposure duration in addition to other mitigation strategies to ascribe the degree of protection afforded. This becomes evident when examining the physical distancing within a well-mixed environment: the effectiveness of physical distancing can diminish.33, 43, 44 Indeed, we observed apparent reduction in time to first contact with increasing ventilation at 0.9 m physical distancing (Figure S3) that was similar to the 1.8 m results. These results demonstrate that a complex interplay between air mixing and exposure duration can determine an individual’s aerosol exposure.

3.3 Limitations

The current investigation has several noteworthy limitations that must be considered. First, the mass concentration of aerosol generated during the experimental modalities, particularly breathing, was higher than those produced from human exhalations.23 The higher concentrations combined with the wide dynamic range of the OPC allowed for stable and reproducible measurements while assuring attainment of quantitative limits of detection among all tests. Second, the simulators lack generation of body heat, do not generate a thermal exhalation plume, and exhale or cough dry salt particles, all of which affect aerosol size, aerosol dispersion, and inhalation exposure.45-47 Given the confines of the environmental chamber, the internal ventilation setup, and the high aerosol concentrations, we would not expect substantial differences in mean mass exposure given the small volume of the chamber. Therefore, limits must be placed on the interpretation of the results within a larger indoor environment, especially considering the dispersion potential of an exhalatory thermal plume and the strong influence of ventilation supply air flow observed. Third, the range of human respiratory aerosols can be smaller and larger than the measured range of this investigation (0.3–3.0 µm).35, 37 For droplets, the effect of physical distancing may be higher than those suggested by the observed results. Fourth, the study investigated the exposure reduction of a single 3-ply cotton mask. The authors recognize the limitation of having tested a single mask, since the effectiveness of exposure reduction by other masks could be either higher or lower, depending on the mask. Nonetheless, the analytics of the study allow for reasonable expectation of exposure reduction of the other predictor variables provided the aerosol behavior does not significantly deviate from this study with another type of mask.

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By
Katie Pearce

/

Published

Feb 22, 2022

In more than 40 million American kitchens, cooking takes place through instantaneous fire—the glowing blue flame of a gas stove. Although it has served as a mainstay appliance for more than a century, the gas stove is now becoming a policy flashpoint as concerns rise over its environmental and health impacts.

Recently, a study from Stanford University presented eye-opening findings on the emissions the appliances release, estimating that all the gas stoves in the U.S. may pose an annual climate impact equal to that of 500,000 cars. The new research comes as dozens of U.S. cities have taken measures to ban gas appliances from new construction and further cut off reliance on fossil fuels. The movement started in Berkeley, California, in 2019, and last month New York City became the largest municipality in the country to do so. Medical experts have also become increasingly outspoken in their warnings about indoor air pollution from gas stoves, which are known to exacerbate and trigger symptoms of asthma and other respiratory issues.

With the prevalence of gas stoves across the U.S., and the costs and infeasibility of replacing them, it’s unlikely to see the appliances disappear anytime soon. The politics are also fraught, with challenges from the fossil fuel industry—and corresponding laws enacted by at least 20 states, including Arizona, Georgia, Ohio, and Texas, to prevent their cities from restricting gas use.

Environmental hazards

From the standpoint of climate change, natural gas is troublesome. A nonrenewable fossil fuel culled from the earth by drilling and hydraulic fracking, natural gas emits two planet-warming greenhouse gases: carbon dioxide (from burning the gas), and methane (from gas leaks). Of the two, methane dissipates more quickly but is 80 times more potent over 20 years, according to the Stanford study.

For cities and states that have set targets for net-zero greenhouse gas emissions, “burning natural gas is incompatible with those goals,” says Scot Miller, a Johns Hopkins assistant professor of environmental health and engineering who studies greenhouse gases and air pollutants. In general, climate-friendly policies emphasize shifting away from burning fossil fuels that produce atmosphere-eroding greenhouse gases and, instead, increasing reliance on electricity that can be powered by renewable sources such as solar and wind. The Biden administration’s climate plan calls for government incentives to induce people to switch from gas in their homes to all-electric.

Video credit: Stanford University

In 2019, natural gas made up 32% of energy consumption in the U.S., according to the Energy Information Administration. By estimates of the same agency in 2015, gas stoves accounted for a slim portion of that, less than 3%. But because methane is so potent, “small leaks can have a significant impact,” Miller says. And leaks can be fairly common along different stages of the natural gas infrastructure, from drilling and piping to the slight but still harmful vapors that exit household stoves, even when turned off, he says.

The recent Stanford study reveals only a hint of the problem inside kitchens, as indoor air quality remains unregulated by the Environmental Protection Agency. However, an EPA spokesperson told The Washington Post last month that the agency intends to update its annual greenhouse gas inventory in 2022 to include emissions from inside homes and buildings.

Health hazards

Natural gas also produces the byproduct of nitrogen dioxide (NO2), an air pollutant that can reduce lung function, cause inflammation of airways, increase asthma attacks, and exacerbate other respiratory issues, according to the American Lung Association. A growing body of medical literature has established a link between cooking with gas and childhood asthma, including one meta-analysis from 2013 finding that children living in homes with gas stoves face a 42% increased risk of asthma symptoms and a 24% increased risk of an asthma diagnosis in their lifetimes. A Johns Hopkins study from 2008 found similar associations.

In January 2020, the stalwart New England Journal of Medicine published an editorial warning against “the false promise” of natural gas, citing “health and environmental hazards and reduced social welfare at every stage of its cycle.”

“I think it’s important that better information gets out, is more publicly available, so people are aware of the potential risks,” says William Checkley, an associate professor of medicine at Johns Hopkins with expertise in acute lung injury.

Checkley emphasizes the importance of proper ventilation, including range hoods above the stove, which “can eliminate a good amount of NO2 and protect people in the kitchen.” However, unlike other gas appliances such as water heaters, federal regulations don’t require gas stoves to vent to the outdoors.

“I think it’s important that better information gets out, is more publicly available, so people are aware of the potential risks.”

William Checkley

Johns Hopkins School of Medicine

A recent UCLA study notes that the effectiveness of ventilation “usually depends on appliance quality and maintenance.” Because of that fact, the health burdens tend to be disproportionately high among low-income citizens, who are more often renters with little choice over their appliances—and more likely to live in small spaces where pollutants are more concentrated.

It’s complicated

Compared to coal and oil, natural gas has been hailed as a cleaner-burning, lower-carbon fuel. During the Obama administration, Miller notes, gas was positioned as an ideal “transition” fuel to bridge between coal and oil of the past and the renewable energy sources that will become more available in the future.

That line of thinking remains realistic in the short term, Miller says, as hopes for a greener electricity grid look 10 to 20 years out. “If we stopped using natural gas in our homes at the moment, we would increase our use of coal,” which is more dangerous for the climate, he says.

“It’s an interesting public policy challenge and really a difficult problem to answer in terms of generating a strong climate strategy,” Miller says.

A similar kind of realism can apply when considering the health impacts. Checkley, who works in global settings including some with extreme poverty, notes that many households rely on burning wood or coal for cooking, posing graver health consequences than gas. “The residents are not able to afford gas, and electricity is not an option because they’re off the grid,” he says. In the U.S., however, “resources are more readily available to move us to cleaner fuels like electricity-based cooking.”

A 2020 report from the Physicians for Social Responsibility and three environmental groups recommended steps an individual can take, when possible, to reduce risks from gas stoves. Hood ranges with fans that vent outdoors are always preferable, and fans are more successful when you use the back burners. If vents aren’t available, try to cook with the windows open. Also try for alternative cooking methods that don’t use gas, including toaster ovens or electric induction cooktops.

While gas stoves themselves might not be the No. 1 concern in terms of pollutants, the larger goal of reducing use of greenhouse gases is of urgent importance, Miller says, as emissions only continue to increase globally. “The question of whether we should get rid of gas stoves and other gas appliances really depends on what we as a society, and our governments, decide is the best path forward for reducing our greenhouse gas emissions,” Miller says. “There’s a lot of debate over which policies are most effective, but personally, I would argue that any measure that helps reduce our emissions is a good measure.”

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Prime Minister Sheikh Hasina yesterday strictly directed the ministries to find ways to gradually get rid of the huge amount of subsidies that have to be spent for various sectors.

The premier came up with the directives while speaking about the subsidies on gas and electricity at a meeting of the Executive Committee of the National Economic Council (Ecnec), said Planning Minister MA Mannan.

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“Put an end to subsidies in phases — gently, economically and effectively,” the planning minister quoted the prime minister as saying.

Usually, the government shells out subsidies amounting to Tk 50,000 to Tk 60,000 crore annually for different sectors. However, the government will have to spend about Tk 70,000 crore in subsidies in the current fiscal year in the gas, fertiliser and electricity sectors alone if prices are not adjusted in line with the global prices.

“The subsidy we give is not fair. Some people are getting this subsidy while others are giving it. This is a serious matter. We must get rid of the system,” said Mannan, while briefing the media after the meeting.

The subsidy on food and the subsidy on air conditioners are completely different, he said.

State Minister for Planning Shamsul Alam said, “We will provide subsidies to farmers and marginalised people. We will not give the subsidy for using AC to the people who live in Gulshan.”

Responding to a query about monitoring development projects by the deputy commissioners (DCs), Abu Hena Morshed Zaman, secretary of the Implementation Monitoring and Evaluation Division (IMED), said the matter of the DCs to monitor projects was nothing new.

The cabinet in 2011 issued a notice, saying that the DCs will monitor projects, he said.

The secretary said there are 590 projects under the annual development programme. “Of them, the IMED has the capacity to monitor 500 projects. The rest are looked after by the DCs.”

Yesterday, the Ecnec approved 10 projects, including two revised ones. The overall estimated cost of the projects will be Tk 8,804.10 crore.

The projects include the “Development of BTCL’s optical fibre transmission network for 5G utilisation” at an estimated cost of Tk 1,059 crore and the “Rehabilitation of coastal dams, development of drainage system and protection of banks in Mujib Nagar and Monpura of Bhola, Phase-I” with an estimated cost of Tk 1,092.70 crore.

Other projects include the rehabilitation of polders and protection of Betagi town and other vulnerable parts from river erosion caused by the Bishkhali and Payra rivers in Barguna with Tk 826.49 crore; integrated community-based centre for child care, protection and swim-safe facilities with Tk 271.83 crore; and establishment of two modern fire stations in Bangabandhu Sheikh Mujib Industrial City for Tk 122.72 crore. 

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