Assessing near-Earth object impact risk: Size classes, probabilities, and urban exposure

Hello,


I have written some interesting articles that are related to my subject of today , and here they are in the following web links, and hope that you will read them carefully:

The Probability of a killer superflare from the Sun: Risks, evidence, and implications

https://myphilo10.blogspot.com/2025/12/the-probability-of-killer-superflare.html

My quick review about my previous article about the comparative risks with the killer solar superflare

https://myphilo10.blogspot.com/2025/12/my-quick-review-about-my-previous.html


So you have to know that the probability of a dangerous supernova hitting Earth in the next few million years is effectively zero. Space is simply too big, and the dangerous stars are too far away. So then the dangerousness from supernova is not the problem , so there remain the Carrington-scale storm and Magnitude 7 volcano eruption that can be dangerous for humanity, so i think we are safe from them for the next 100 years and you can read about it in my second article above. So , then , there remain the Asteroid impacts of 1km and more that are very rare , so they are not a problem, and i have talked about it in my above second article. So , then , there remain the Asteroid impacts for 1 km and less that i will talk about in my below new paper of today , but i think we are safe from them for the next 100 years and you can notice it by reading my below new paper of today , and of course notice that all my papers and articles are verified and rated by the advanced AIs such as Gemini 3.0 Pro, so here is my below thoughts about it:

So in my below new paper , in the below table , the "Global Impact Prob. (100yr)" , means the global impact probability for the next 100 years , and the "Urban Impact Prob (Land ~3%)" , means the impact probability for the next 100 years in the cities that only occupy 3% of the Earth's land , so then the "Urban Impact Prob (Land ~3%)" means the following:

"I calculated this number by taking the chance of a Land Impact and multiplying it by 0.03, because cities only occupy 3% of the Earth's land."

So from this , you can deduce the probabilities for the global land's impact for the next 100 years from: "The Rule of Three".

And for today , here is my below new interesting paper called: "**Assessing Near-Earth Object Impact Risk: Size Classes, Probabilities, and Urban Exposure**":

And here is my new paper:

---

# **Assessing Near-Earth Object Impact Risk: Size Classes, Probabilities, and Urban Exposure**

## **Abstract**

Near-Earth objects (NEOs) periodically intersect Earth’s orbit, posing risks that vary drastically based on size, composition, impact location, and **entry geometry**. Public discourse frequently conflates **global impact probability** (the chance of an event occurring anywhere on Earth) with **human or urban risk** (the chance of casualties), leading to exaggerated perceptions of danger. This paper presents a structured assessment of asteroid size classes over a 100-year horizon, derived from current NEO population models (Harris & D’Abramo, 2015). By applying "layered probability" filters—incorporating ocean depth, land distribution, **economic/urban density**, and atmospheric entry physics—we demonstrate that the probability of a direct hit on a major metropolitan area is orders of magnitude lower than the global impact probability. The results support a bifurcated planetary defense strategy: deflection for larger, tracked objects, and civil defense for smaller, surprise impactors.

---

## **1. Introduction**

Asteroid impacts have driven significant geological and biological events throughout Earth's history. However, humanity now possesses the unique ability to quantify this risk. Utilizing data from surveys such as NASA’s Center for Near-Earth Object Studies (CNEOS) and the Catalina Sky Survey, we can distinguish between the statistical certainty of impacts over geological time and the low probability of catastrophe on human timescales.

This paper addresses a fundamental misunderstanding in risk communication:

> The probability of an asteroid hitting Earth is **not** the same as the probability of it causing a human catastrophe.

We argue that risk assessment must account for "protective layers": atmospheric shielding, the nuance of deep-ocean vs. coastal impacts, and the sparse distribution of urban centers.

---

## **2. Methodology and Classification of Near-Earth Objects**

Our frequency estimates rely on **power-law population models**, which estimate the number of undiscovered NEOs based on the detection rates of known objects.

### **2.1 The Role of Composition and Geometry**

Size is not the sole determinant of danger. Two critical physical factors act as "risk multipliers" or "dampeners":

1. **Composition:**

* **Stony / "Rubble Pile" (sg ~ 1.5–2.5 g/cm^3):** The most common type. Prone to high-altitude airbursts (fragmentation), dissipating energy laterally.
* **Iron / Metallic (sg ~ 7.0–8.0 g/cm^3):** Rare (~5% of population). These denser objects can punch through the atmosphere to create impact craters even at smaller sizes (e.g., **Meteor Crater, Arizona**, created by a ~30–50m iron impactor).

2. **Entry Angle:**

* **Shallow Entry (<30 degrees):** Increases the path length through the atmosphere, subjecting the object to greater friction and stress. This significantly increases the probability of a high-altitude airburst, reducing ground damage.
* **Steep Entry (>45 degrees):** The object traverses the atmosphere quickly, preserving kinetic energy for a lower airburst or ground impact.

### **2.2 Size Class Analysis & Warning Horizons**

#### **Class A: Objects <10 meters**

* **Frequency:** Daily to weekly.
* **Fate:** Complete atmospheric disintegration (bolides).
* **Warning Horizon:** None (detected upon entry).
* **Risk:** **Negligible**.

#### **Class B: 10–30 meters**

* **Frequency:** Every 10–50 years.
* **Fate:** Mid-to-high altitude airburst.
* **Reference Event:** **Chelyabinsk (2013)**. Approx. 20m; released ~440 kilotons.
* **Warning Horizon:** **Hours to Days.** Objects of this size are often too faint to detect until they are very close to Earth (e.g., asteroid 2023 CX1).
* **Risk:** Low fatality risk; moderate injury risk via glass/debris.

#### **Class C: 30–50 meters**

* **Frequency:** Every 100–500 years.
* **Fate:** Low-altitude airburst (stony) or surface cratering (iron).
* **Reference Event:** **Tunguska (1908)**. Flattened ~2,000 km^2 of forest.
* **Warning Horizon:** **Days to Weeks.** Still difficult to track long-term due to faint magnitude.
* **Risk:** **Moderate to High (Context Dependent).** An airburst over a city would result in mass casualties from **thermal radiation** and **overpressure shockwaves**.

#### **Class D: 50–100 meters**

* **Frequency:** Every 1,000–5,000 years.
* **Global Probability (100-year):** **~2–10%**
* **Damage Potential:** "City-killer" scale if a direct hit occurs.
* **Warning Horizon:** **Months to Years.** Large enough to be cataloged by next-gen surveyors (e.g., NEO Surveyor).
* **Status:** The primary target for modern deflection strategies.

#### **Class E: 100–300 meters**

* **Frequency:** Every 10,000–100,000 years.
* **Global Probability (100-year):** **~0.1–1%**
* **Damage Potential:** Sub-continental devastation; significant tsunami risk if impacting the continental shelf.
* **Warning Horizon:** **Decades.** We have cataloged a significant percentage of these; orbit determination allows for long lead times.

#### **Class F: >1 Kilometer**

* **Global Probability (100-year):** **<0.001%**
* **Damage Potential:** Global climate collapse.
* **Warning Horizon:** **Centuries.** >95% are tracked; none pose an immediate threat.

---

## **3. Layered Probability Analysis**

To assess actual risk, we must apply geographical and physical filters to the global probabilities cited above.

### **3.1 Ocean Shielding and The "Shelf" Exception**

Oceans cover **~71%** of Earth.
* **Deep Ocean:** For objects <300m, deep water acts as a buffer. The *Van Dorn effect* suggests that tsunamis generated by point-source impacts dissipate rapidly over distance compared to seismic tsunamis.
* **Continental Shelf:** Impacts here remove the buffer of distance and depth, posing a severe threat to coastlines even without a land strike.

### **3.2 The "Urban" vs. "Economic" Target**

There is a distinction between **Casualty Risk** (hitting a dense city) and **Economic Risk** (hitting suburbs or infrastructure).
* **High-Density Urban (~1%):** The "bullseye" for mass casualty events is remarkably small.
* **Built-Up/Economic Zones (~3–4%):** Including suburbs, industrial zones, and farmland, the target grows. A strike here might have a low death toll (due to lower density and evacuation potential) but could cause **catastrophic economic loss** (hundreds of billions in infrastructure damage).
* **Result:** Even if an object strikes land (a 29% chance), there is a >90% chance it strikes uninhabited territory (deserts, tundra, forests).

### **3.3 Atmospheric Filtration**

For stony objects <50m, the atmosphere acts as a kinetic shield, converting kinetic energy into thermal radiation and shockwaves (airbursts) rather than ground coupling. This reduces the lethality radius but extends the "blast zone" for window breakage and structural damage.

---

## **4. Combined Risk Estimates**

Combining population models with geographical filters yields the following *effective* risks per century.

- Object Size	- Global Impact Prob. (100yr)	- Typical Warning Time	- Impact Consequence	- Urban Impact Prob. (Land ~3%)
**30–50 m**	~20–30%	Hours – Days	Regional Shockwave / Localized	**~0.2%**
**50–100 m**	~2–10%	Months – Years	City-Killer / Regional Tsunami	**~0.01–0.1%**
**100–300 m**	~0.1–1%	Years – Decades	Sub-Continental / Climate Effect	**~0.001–0.01%**
**> 1 km**	<0.001%	Decades+	Global Extinction	**Negligible**

*Note: While the probability of an Urban Impact is low, the "Expected Value" (Probability Loss) remains high due to the potential for millions of casualties in a worst-case scenario.*

---

## **5. Limitations and Uncertainties**

1. **Incomplete Catalogues:** While >95% of 1km+ objects are found, less than 40% of objects in the 140m range are currently tracked (NASA/PDCO, 2021).
2. **The "Blind Spot":** Objects approaching from the direction of the Sun (e.g., daytime side) are currently undetectable by Earth-based optical telescopes until moments before impact.
3. **Critical Infrastructure Fragility:** Our model focuses on population density. It does not account for strikes on low-density but high-criticality targets—such as **nuclear power stations, hydroelectric dams, or undersea cable hubs**.
4. **Cascading Effects:** This paper focuses on direct impact damage, not the complex market collapses or supply chain failures resulting from a remote impact.

---

## **6. Implications for Planetary Defense**

The data supports a **Bifurcated Strategy** dependent on size and warning time:

1. **Class D, E, F (Large / Long Warning): Deflection.**
The success of **NASA’s DART mission (2022)** proves that kinetic impactors can alter trajectories. Because these objects are usually detected years in advance, deflection is the primary strategy.

2. **Class B, C (Small / Short Warning): Civil Defense.**
For objects <50m, we may only have days or hours of warning. Deflection is impossible on this timeline. Risk mitigation must rely on **evacuation protocols** (for impact zones) and **shelter-in-place orders** (to protect against glass/shockwave injuries in the blast radius).

---

## **7. Conclusion**

Asteroid impact risk is real, but strictly constrained by size frequency and Earth’s geography. While sub-kilometer objects possess the energy to cause regional disasters, the probability of a direct strike on a major population center is **orders of magnitude smaller** than the global impact probability suggests.

By acknowledging the protective roles of the atmosphere, the deep oceans, and the difference between **economic damage** and **mass casualties**, we can move the public narrative from existential dread to rational support for planetary defense engineering: **Deflection** for the planet-killers, and **Civil Defense** for the city-shakers.

---

And here is what the AI (Artificial intelligence) from Google called Gemini 3.0 Pro , that is advanced , answered me about my above new paper:

"This is a very strong draft. It reads like a high-level executive summary or a white paper designed for policy makers or risk management professionals.

The core strength of this paper is that it successfully bridges the gap between **astrophysics** (what is out there) and **actuarial science** (what it means for us). Most pop-science articles stop at "Global Probability," while your paper introduces the necessary geographic and demographic filters to create a realistic risk profile.

Here is a detailed breakdown of my thoughts, organized by strengths, technical feedback, and suggestions for refinement.

---

### **1. Strengths**

* **The "Layered Probability" Concept:** This is the strongest intellectual contribution of the paper. By filtering global probabilities through ocean coverage and urban density, you de-escalate the panic without dismissing the risk. This is a crucial distinction for public communication.
* **The Bifurcated Strategy:** The conclusion (Deflection vs. Civil Defense) is logically sound and follows directly from your data. It solves the "what do we do?" problem effectively by acknowledging that we cannot deflect the small, surprise rocks (Class B/C).
* **Physical Nuance:** You didn't just look at size; you included **composition** (stony vs. iron) and **entry angle**. This adds significant credibility. The mention of the "Van Dorn effect" regarding tsunamis is a sophisticated detail that shows you have done your homework on hydrocodes.
* **Readable Structure:** The classification system (Classes A–F) is intuitive and makes the table in Section 4 very easy to parse.

---

### **2. Technical & Content Critique**

Here are specific areas where you might want to tighten the science or clarify the arguments:

**A. The "Van Dorn Effect" & Tsunami Risk (Section 3.1)**
* **Critique:** While deep-ocean impacts dissipate energy efficiently (Van Dorn), be careful not to underplay the **Continental Shelf** risk.
* **Suggestion:** You mention the "shelf exception," but emphasize that a shelf impact near a coast (e.g., off the East Coast of the US or Japan) could be *worse* than a land impact due to the efficiency of water in transferring kinetic energy into infrastructure damage (inundation) compared to an airburst.

**B. The "Blind Spot" & Future Tech (Section 5)**
* **Critique:** You mention the solar blind spot.
* **Suggestion:** You should explicitly mention the **NEO Surveyor** (NASA) or **NEOMIR** (ESA) missions here. These are space-based infrared telescopes specifically designed to sit at Lagrange points to look closer to the sun. Mentioning them transforms the "limitation" into a "solvable engineering problem."

**C. The LSST / Vera Rubin Observatory**
* **Critique:** In Section 2.2 (Class B/C Warning Horizon), you state "Hours to Days."
* **Suggestion:** You should add a note about the **Vera Rubin Observatory (LSST)** coming online. It is expected to drastically increase the detection rate of Class C and D objects, potentially shifting the warning horizon from "weeks" to "months/years" for some smaller objects.

**D. The "Urban" Definition (Section 3.2)**
* **Critique:** You use a ~1% figure for high-density urban and ~3-4% for economic zones.
* **Suggestion:** State your source or methodology for this roughly. Is this based on the *Gridded Population of the World (GPW)* data? Clarifying that "Urban" implies a specific population density (e.g., >1,000 people/km ) would make this more robust.

---

### **3. Structural & Stylistic Suggestions**

**Title Tweak**
* *Current:* Assessing Near-Earth Object Impact Risk: Size Classes, Probabilities, and Urban Exposure.
* *Suggestion:* The title is good, but "Urban Exposure" sounds slightly passive. Perhaps: **"Assessing NEO Impact Risk: Filtering Global Probabilities through Urban and Geographic Exposure."**

**Table Refinement (Section 4)**
* **Column Header:** Change "Global Impact Prob. (100yr)" to **"Global Impact Probability (per Century)."** It sounds more academic.
* **Clarification:** For Class B (30-50m), the Urban Impact Probability is listed as ~0.2%. Make sure the text clarifies that this is a 0.2% chance *per century* of a city-killing airburst. (0.2% is actually quite high in risk management terms—comparable to certain 100-year flood risks—so this deserves a sentence of context).

**Abstract Optimization**
* Your abstract is strong, but the sentence starting with *"By applying 'layered probability' filters..."* is quite dense. You might break it into two sentences to let the "bifurcated strategy" stand out more.

---

### **4. The "Missing" Variable: Critical Infrastructure**

In Section 3.2 or 5, you briefly touch on "Economic Risk." I would suggest adding a specific sentence about **Fragility**.
* A 50m airburst over a forest is a 0 on the disaster scale.
* A 50m airburst over a semi-conductor factory or a major cloud-computing server farm could cause a global economic recession.
* *Add:* "Modern society has created 'fragility nodes'—specific geographic points (e.g., Taiwaneses chip foundries, major internet exchanges) where a localized impact would have outsized global cascading effects."

### **Final Verdict**

This is a **publishable quality draft** for a policy journal, a risk management periodical, or a general science outlet. It corrects a major flaw in public discourse (size = death) and replaces it with a nuanced, actionable framework.

**Grade: A-** (Would be an A+ with citations for the land-use percentages and a mention of LSST/NEO Surveyor)."


Thank you,
Amine Moulay Ramdane.